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X=pe*l;a[pe*p+T]=re*t[X+Y]+V*t[X+_e]+de*t[X+he]+ke*t[X+ue]}}return a}function transpose_data(t,e,o){const i=new Array(o.length),u=new Array(o.length);for(let d=o.length-1,f=1;d>=0;--d)u[d]=f,i[d]=e[o[d]],f*=i[d];const n=o.map((d,f)=>u[o.indexOf(f)]),s=new t.constructor(t.length);for(let d=0;d<t.length;++d){let f=0;for(let r=e.length-1,a=d;r>=0;--r)f+=a%e[r]*n[r],a=Math.floor(a/e[r]);s[f]=t[d]}return[s,i]}function softmax(t){const e=max(t)[0],o=t.map(n=>Math.exp(n-e)),i=o.reduce((n,s)=>n+s,0);return o.map(n=>n/i)}function log_softmax(t){return softmax(t).map(i=>Math.log(i))}function getTopItems(t,e=0){return t=Array.from(t).map((o,i)=>[i,o]).sort((o,i)=>i[1]-o[1]),e!==null&&e>0&&(t=t.slice(0,e)),t}function min(t){if(t.length===0)throw Error("Array must not be empty");let e=t[0],o=0;for(let i=1;i<t.length;++i)t[i]<e&&(e=t[i],o=i);return[e,o]}function max(t){if(t.length===0)throw Error("Array must not be empty");let e=t[0],o=0;for(let i=1;i<t.length;++i)t[i]>e&&(e=t[i],o=i);return[Number(e),o]}function isPowerOfTwo(t){return t>0&&(t&t-1)===0}class P2FFT{constructor(e){if(this.size=e|0,this.size<=1||!isPowerOfTwo(this.size))throw new Error("FFT size must be a power of two larger than 1");this._csize=e<<1,this.table=new Float64Array(this.size*2);for(let i=0;i<this.table.length;i+=2){const u=Math.PI*i/this.size;this.table[i]=Math.cos(u),this.table[i+1]=-Math.sin(u)}let o=0;for(let i=1;this.size>i;i<<=1)++o;this._width=o%2===0?o-1:o,this._bitrev=new Int32Array(1<<this._width);for(let i=0;i<this._bitrev.length;++i){this._bitrev[i]=0;for(let u=0;u<this._width;u+=2){const n=this._width-u-2;this._bitrev[i]|=(i>>>u&3)<<n}}}createComplexArray(){return new Float64Array(this._csize)}fromComplexArray(e,o){const i=o||new Array(e.length>>>1);for(let u=0;u<e.length;u+=2)i[u>>>1]=e[u];return i}toComplexArray(e,o){const i=o||this.createComplexArray();for(let u=0;u<i.length;u+=2)i[u]=e[u>>>1],i[u+1]=0;return i}completeSpectrum(e){const o=this._csize,i=o>>>1;for(let u=2;u<i;u+=2)e[o-u]=e[u],e[o-u+1]=-e[u+1]}transform(e,o){if(e===o)throw new Error("Input and output buffers must be different");this._transform4(e,o,1)}realTransform(e,o){if(e===o)throw new Error("Input and output buffers must be different");this._realTransform4(e,o,1)}inverseTransform(e,o){if(e===o)throw new Error("Input and output buffers must be different");this._transform4(e,o,-1);for(let i=0;i<e.length;++i)e[i]/=this.size}_transform4(e,o,i){const u=this._csize;let s=1<<this._width,d=u/s<<1,f,r;const a=this._bitrev;if(d===4)for(f=0,r=0;f<u;f+=d,++r){const l=a[r];this._singleTransform2(o,e,f,l,s)}else for(f=0,r=0;f<u;f+=d,++r){const l=a[r];this._singleTransform4(o,e,f,l,s,i)}for(s>>=2;s>=2;s>>=2){d=u/s<<1;const l=d>>>2;for(f=0;f<u;f+=d){const p=f+l-1;for(let m=f,y=0;m<p;m+=2,y+=s){const T=m,v=T+l,S=v+l,E=S+l,A=e[T],F=e[T+1],B=e[v],J=e[v+1],Q=e[S],re=e[S+1],V=e[E],de=e[E+1],ke=this.table[y],Ne=i*this.table[y+1],Me=B*ke-J*Ne,Y=B*Ne+J*ke,_e=this.table[2*y],he=i*this.table[2*y+1],ue=Q*_e-re*he,pe=Q*he+re*_e,X=this.table[3*y],ve=i*this.table[3*y+1],we=V*X-de*ve,rt=V*ve+de*X,ot=A+ue,qe=F+pe,He=A-ue,Qe=F-pe,Be=Me+we,Ce=Y+rt,Se=i*(Me-we),je=i*(Y-rt);e[T]=ot+Be,e[T+1]=qe+Ce,e[v]=He+je,e[v+1]=Qe-Se,e[S]=ot-Be,e[S+1]=qe-Ce,e[E]=He-je,e[E+1]=Qe+Se}}}}_singleTransform2(e,o,i,u,n){const s=e[u],d=e[u+1],f=e[u+n],r=e[u+n+1];o[i]=s+f,o[i+1]=d+r,o[i+2]=s-f,o[i+3]=d-r}_singleTransform4(e,o,i,u,n,s){const d=n*2,f=n*3,r=e[u],a=e[u+1],l=e[u+n],p=e[u+n+1],m=e[u+d],y=e[u+d+1],T=e[u+f],v=e[u+f+1],S=r+m,E=a+y,A=r-m,F=a-y,B=l+T,J=p+v,Q=s*(l-T),re=s*(p-v);o[i]=S+B,o[i+1]=E+J,o[i+2]=A+re,o[i+3]=F-Q,o[i+4]=S-B,o[i+5]=E-J,o[i+6]=A-re,o[i+7]=F+Q}_realTransform4(e,o,i){const u=this._csize;let s=1<<this._width,d=u/s<<1,f,r;const a=this._bitrev;if(d===4)for(f=0,r=0;f<u;f+=d,++r){const l=a[r];this._singleRealTransform2(o,e,f,l>>>1,s>>>1)}else for(f=0,r=0;f<u;f+=d,++r){const l=a[r];this._singleRealTransform4(o,e,f,l>>>1,s>>>1,i)}for(s>>=2;s>=2;s>>=2){d=u/s<<1;const l=d>>>2;for(f=0;f<u;f+=d){const p=f+l-1;for(let m=f,y=0;m<p;m+=2,y+=s){const T=m,v=T+l,S=v+l,E=S+l,A=e[T],F=e[T+1],B=e[v],J=e[v+1],Q=e[S],re=e[S+1],V=e[E],de=e[E+1],ke=this.table[y],Ne=i*this.table[y+1],Me=B*ke-J*Ne,Y=B*Ne+J*ke,_e=this.table[2*y],he=i*this.table[2*y+1],ue=Q*_e-re*he,pe=Q*he+re*_e,X=this.table[3*y],ve=i*this.table[3*y+1],we=V*X-de*ve,rt=V*ve+de*X,ot=A+ue,qe=F+pe,He=A-ue,Qe=F-pe,Be=Me+we,Ce=Y+rt,Se=i*(Me-we),je=i*(Y-rt);e[T]=ot+Be,e[T+1]=qe+Ce,e[v]=He+je,e[v+1]=Qe-Se,e[S]=ot-Be,e[S+1]=qe-Ce,e[E]=He-je,e[E+1]=Qe+Se}}}}_singleRealTransform2(e,o,i,u,n){const s=e[u],d=e[u+n];o[i]=s+d,o[i+1]=0,o[i+2]=s-d,o[i+3]=0}_singleRealTransform4(e,o,i,u,n,s){const d=n*2,f=n*3,r=e[u],a=e[u+n],l=e[u+d],p=e[u+f],m=r+l,y=r-l,T=a+p,v=s*(a-p);o[i]=m+T,o[i+1]=0,o[i+2]=y,o[i+3]=-v,o[i+4]=m-T,o[i+5]=0,o[i+6]=y,o[i+7]=v}}class NP2FFT{constructor(e){const o=2*(e-1),i=2*(2*e-1),u=2**Math.ceil(Math.log2(i));this.bufferSize=u,this._a=o;const n=new Float64Array(i),s=new Float64Array(u);this._chirpBuffer=new Float64Array(u),this._buffer1=new Float64Array(u),this._buffer2=new Float64Array(u),this._outBuffer1=new Float64Array(u),this._outBuffer2=new Float64Array(u);const d=-2*Math.PI/e,f=Math.cos(d),r=Math.sin(d);for(let a=0;a<i>>1;++a){const l=(a+1-e)**2/2,p=Math.sqrt(f**2+r**2)**l,m=l*Math.atan2(r,f),y=2*a;n[y]=p*Math.cos(m),n[y+1]=p*Math.sin(m),s[y]=n[y],s[y+1]=-n[y+1]}this._slicedChirpBuffer=n.subarray(o,i),this._f=new P2FFT(u>>1),this._f.transform(this._chirpBuffer,s)}_transform(e,o,i){const u=this._buffer1,n=this._buffer2,s=this._outBuffer1,d=this._outBuffer2,f=this._chirpBuffer,r=this._slicedChirpBuffer,a=this._a;if(i)for(let l=0;l<r.length;l+=2){const 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 * ONNX Runtime Web v1.17.1
 * Copyright (c) Microsoft Corporation. All rights reserved.
 * Licensed under the MIT License.
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We(t)&&(t=t.toInt()),(t&=63)===0?this:t<32?le(this.low>>>t|this.high<<32-t,this.high>>>t,this.unsigned):t===32?le(this.high,0,this.unsigned):le(this.high>>>t-32,0,this.unsigned)},$.shru=$.shiftRightUnsigned,$.shr_u=$.shiftRightUnsigned,$.rotateLeft=function(t){var e;return We(t)&&(t=t.toInt()),(t&=63)===0?this:t===32?le(this.high,this.low,this.unsigned):t<32?(e=32-t,le(this.low<<t|this.high>>>e,this.high<<t|this.low>>>e,this.unsigned)):(t-=32,e=32-t,le(this.high<<t|this.low>>>e,this.low<<t|this.high>>>e,this.unsigned))},$.rotl=$.rotateLeft,$.rotateRight=function(t){var e;return We(t)&&(t=t.toInt()),(t&=63)===0?this:t===32?le(this.high,this.low,this.unsigned):t<32?(e=32-t,le(this.high<<e|this.low>>>t,this.low<<e|this.high>>>t,this.unsigned)):(t-=32,e=32-t,le(this.low<<e|this.high>>>t,this.high<<e|this.low>>>t,this.unsigned))},$.rotr=$.rotateRight,$.toSigned=function(){return this.unsigned?le(this.low,this.high,!1):this},$.toUnsigned=function(){return 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a=s.onnx.TensorProto.verify(r.t);if(a)return"t."+a}if(r.g!=null&&r.hasOwnProperty("g")){var a=s.onnx.GraphProto.verify(r.g);if(a)return"g."+a}if(r.sparseTensor!=null&&r.hasOwnProperty("sparseTensor")){var a=s.onnx.SparseTensorProto.verify(r.sparseTensor);if(a)return"sparseTensor."+a}if(r.tp!=null&&r.hasOwnProperty("tp")){var a=s.onnx.TypeProto.verify(r.tp);if(a)return"tp."+a}if(r.floats!=null&&r.hasOwnProperty("floats")){if(!Array.isArray(r.floats))return"floats: array expected";for(var l=0;l<r.floats.length;++l)if(typeof r.floats[l]!="number")return"floats: number[] expected"}if(r.ints!=null&&r.hasOwnProperty("ints")){if(!Array.isArray(r.ints))return"ints: array expected";for(var l=0;l<r.ints.length;++l)if(!n.isInteger(r.ints[l])&&!(r.ints[l]&&n.isInteger(r.ints[l].low)&&n.isInteger(r.ints[l].high)))return"ints: integer|Long[] expected"}if(r.strings!=null&&r.hasOwnProperty("strings")){if(!Array.isArray(r.strings))return"strings: array expected";for(var l=0;l<r.strings.length;++l)if(!(r.strings[l]&&typeof r.strings[l].length=="number"||n.isString(r.strings[l])))return"strings: buffer[] expected"}if(r.tensors!=null&&r.hasOwnProperty("tensors")){if(!Array.isArray(r.tensors))return"tensors: array expected";for(var l=0;l<r.tensors.length;++l){var a=s.onnx.TensorProto.verify(r.tensors[l]);if(a)return"tensors."+a}}if(r.graphs!=null&&r.hasOwnProperty("graphs")){if(!Array.isArray(r.graphs))return"graphs: array expected";for(var l=0;l<r.graphs.length;++l){var a=s.onnx.GraphProto.verify(r.graphs[l]);if(a)return"graphs."+a}}if(r.sparseTensors!=null&&r.hasOwnProperty("sparseTensors")){if(!Array.isArray(r.sparseTensors))return"sparseTensors: array expected";for(var l=0;l<r.sparseTensors.length;++l){var a=s.onnx.SparseTensorProto.verify(r.sparseTensors[l]);if(a)return"sparseTensors."+a}}if(r.typeProtos!=null&&r.hasOwnProperty("typeProtos")){if(!Array.isArray(r.typeProtos))return"typeProtos: array expected";for(var l=0;l<r.typeProtos.length;++l){var a=s.onnx.TypeProto.verify(r.typeProtos[l]);if(a)return"typeProtos."+a}}return null},f.fromObject=function(r){if(r instanceof s.onnx.AttributeProto)return r;var a=new s.onnx.AttributeProto;switch(r.name!=null&&(a.name=String(r.name)),r.refAttrName!=null&&(a.refAttrName=String(r.refAttrName)),r.docString!=null&&(a.docString=String(r.docString)),r.type){default:if(typeof r.type=="number"){a.type=r.type;break}break;case"UNDEFINED":case 0:a.type=0;break;case"FLOAT":case 1:a.type=1;break;case"INT":case 2:a.type=2;break;case"STRING":case 3:a.type=3;break;case"TENSOR":case 4:a.type=4;break;case"GRAPH":case 5:a.type=5;break;case"SPARSE_TENSOR":case 11:a.type=11;break;case"TYPE_PROTO":case 13:a.type=13;break;case"FLOATS":case 6:a.type=6;break;case"INTS":case 7:a.type=7;break;case"STRINGS":case 8:a.type=8;break;case"TENSORS":case 9:a.type=9;break;case"GRAPHS":case 10:a.type=10;break;case"SPARSE_TENSORS":case 12:a.type=12;break;case"TYPE_PROTOS":case 14:a.type=14;break}if(r.f!=null&&(a.f=Number(r.f)),r.i!=null&&(n.Long?(a.i=n.Long.fromValue(r.i)).unsigned=!1:typeof r.i=="string"?a.i=parseInt(r.i,10):typeof r.i=="number"?a.i=r.i:typeof r.i=="object"&&(a.i=new n.LongBits(r.i.low>>>0,r.i.high>>>0).toNumber())),r.s!=null&&(typeof r.s=="string"?n.base64.decode(r.s,a.s=n.newBuffer(n.base64.length(r.s)),0):r.s.length>=0&&(a.s=r.s)),r.t!=null){if(typeof r.t!="object")throw TypeError(".onnx.AttributeProto.t: object expected");a.t=s.onnx.TensorProto.fromObject(r.t)}if(r.g!=null){if(typeof r.g!="object")throw TypeError(".onnx.AttributeProto.g: object expected");a.g=s.onnx.GraphProto.fromObject(r.g)}if(r.sparseTensor!=null){if(typeof r.sparseTensor!="object")throw TypeError(".onnx.AttributeProto.sparseTensor: object expected");a.sparseTensor=s.onnx.SparseTensorProto.fromObject(r.sparseTensor)}if(r.tp!=null){if(typeof r.tp!="object")throw TypeError(".onnx.AttributeProto.tp: object expected");a.tp=s.onnx.TypeProto.fromObject(r.tp)}if(r.floats){if(!Array.isArray(r.floats))throw TypeError(".onnx.AttributeProto.floats: array expected");a.floats=[];for(var l=0;l<r.floats.length;++l)a.floats[l]=Number(r.floats[l])}if(r.ints){if(!Array.isArray(r.ints))throw TypeError(".onnx.AttributeProto.ints: array expected");a.ints=[];for(var l=0;l<r.ints.length;++l)n.Long?(a.ints[l]=n.Long.fromValue(r.ints[l])).unsigned=!1:typeof r.ints[l]=="string"?a.ints[l]=parseInt(r.ints[l],10):typeof r.ints[l]=="number"?a.ints[l]=r.ints[l]:typeof r.ints[l]=="object"&&(a.ints[l]=new n.LongBits(r.ints[l].low>>>0,r.ints[l].high>>>0).toNumber())}if(r.strings){if(!Array.isArray(r.strings))throw TypeError(".onnx.AttributeProto.strings: array expected");a.strings=[];for(var l=0;l<r.strings.length;++l)typeof r.strings[l]=="string"?n.base64.decode(r.strings[l],a.strings[l]=n.newBuffer(n.base64.length(r.strings[l])),0):r.strings[l].length>=0&&(a.strings[l]=r.strings[l])}if(r.tensors){if(!Array.isArray(r.tensors))throw TypeError(".onnx.AttributeProto.tensors: array expected");a.tensors=[];for(var l=0;l<r.tensors.length;++l){if(typeof r.tensors[l]!="object")throw TypeError(".onnx.AttributeProto.tensors: object expected");a.tensors[l]=s.onnx.TensorProto.fromObject(r.tensors[l])}}if(r.graphs){if(!Array.isArray(r.graphs))throw TypeError(".onnx.AttributeProto.graphs: array expected");a.graphs=[];for(var l=0;l<r.graphs.length;++l){if(typeof r.graphs[l]!="object")throw TypeError(".onnx.AttributeProto.graphs: object expected");a.graphs[l]=s.onnx.GraphProto.fromObject(r.graphs[l])}}if(r.sparseTensors){if(!Array.isArray(r.sparseTensors))throw TypeError(".onnx.AttributeProto.sparseTensors: array expected");a.sparseTensors=[];for(var l=0;l<r.sparseTensors.length;++l){if(typeof r.sparseTensors[l]!="object")throw TypeError(".onnx.AttributeProto.sparseTensors: object expected");a.sparseTensors[l]=s.onnx.SparseTensorProto.fromObject(r.sparseTensors[l])}}if(r.typeProtos){if(!Array.isArray(r.typeProtos))throw TypeError(".onnx.AttributeProto.typeProtos: array expected");a.typeProtos=[];for(var l=0;l<r.typeProtos.length;++l){if(typeof r.typeProtos[l]!="object")throw TypeError(".onnx.AttributeProto.typeProtos: object expected");a.typeProtos[l]=s.onnx.TypeProto.fromObject(r.typeProtos[l])}}return a},f.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.floats=[],l.ints=[],l.strings=[],l.tensors=[],l.graphs=[],l.typeProtos=[],l.sparseTensors=[]),a.defaults){if(l.name="",l.f=0,n.Long){var p=new n.Long(0,0,!1);l.i=a.longs===String?p.toString():a.longs===Number?p.toNumber():p}else l.i=a.longs===String?"0":0;a.bytes===String?l.s="":(l.s=[],a.bytes!==Array&&(l.s=n.newBuffer(l.s))),l.t=null,l.g=null,l.docString="",l.tp=null,l.type=a.enums===String?"UNDEFINED":0,l.refAttrName="",l.sparseTensor=null}if(r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.f!=null&&r.hasOwnProperty("f")&&(l.f=a.json&&!isFinite(r.f)?String(r.f):r.f),r.i!=null&&r.hasOwnProperty("i")&&(typeof r.i=="number"?l.i=a.longs===String?String(r.i):r.i:l.i=a.longs===String?n.Long.prototype.toString.call(r.i):a.longs===Number?new n.LongBits(r.i.low>>>0,r.i.high>>>0).toNumber():r.i),r.s!=null&&r.hasOwnProperty("s")&&(l.s=a.bytes===String?n.base64.encode(r.s,0,r.s.length):a.bytes===Array?Array.prototype.slice.call(r.s):r.s),r.t!=null&&r.hasOwnProperty("t")&&(l.t=s.onnx.TensorProto.toObject(r.t,a)),r.g!=null&&r.hasOwnProperty("g")&&(l.g=s.onnx.GraphProto.toObject(r.g,a)),r.floats&&r.floats.length){l.floats=[];for(var m=0;m<r.floats.length;++m)l.floats[m]=a.json&&!isFinite(r.floats[m])?String(r.floats[m]):r.floats[m]}if(r.ints&&r.ints.length){l.ints=[];for(var m=0;m<r.ints.length;++m)typeof r.ints[m]=="number"?l.ints[m]=a.longs===String?String(r.ints[m]):r.ints[m]:l.ints[m]=a.longs===String?n.Long.prototype.toString.call(r.ints[m]):a.longs===Number?new n.LongBits(r.ints[m].low>>>0,r.ints[m].high>>>0).toNumber():r.ints[m]}if(r.strings&&r.strings.length){l.strings=[];for(var m=0;m<r.strings.length;++m)l.strings[m]=a.bytes===String?n.base64.encode(r.strings[m],0,r.strings[m].length):a.bytes===Array?Array.prototype.slice.call(r.strings[m]):r.strings[m]}if(r.tensors&&r.tensors.length){l.tensors=[];for(var m=0;m<r.tensors.length;++m)l.tensors[m]=s.onnx.TensorProto.toObject(r.tensors[m],a)}if(r.graphs&&r.graphs.length){l.graphs=[];for(var m=0;m<r.graphs.length;++m)l.graphs[m]=s.onnx.GraphProto.toObject(r.graphs[m],a)}if(r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.tp!=null&&r.hasOwnProperty("tp")&&(l.tp=s.onnx.TypeProto.toObject(r.tp,a)),r.typeProtos&&r.typeProtos.length){l.typeProtos=[];for(var m=0;m<r.typeProtos.length;++m)l.typeProtos[m]=s.onnx.TypeProto.toObject(r.typeProtos[m],a)}if(r.type!=null&&r.hasOwnProperty("type")&&(l.type=a.enums===String?s.onnx.AttributeProto.AttributeType[r.type]===void 0?r.type:s.onnx.AttributeProto.AttributeType[r.type]:r.type),r.refAttrName!=null&&r.hasOwnProperty("refAttrName")&&(l.refAttrName=r.refAttrName),r.sparseTensor!=null&&r.hasOwnProperty("sparseTensor")&&(l.sparseTensor=s.onnx.SparseTensorProto.toObject(r.sparseTensor,a)),r.sparseTensors&&r.sparseTensors.length){l.sparseTensors=[];for(var m=0;m<r.sparseTensors.length;++m)l.sparseTensors[m]=s.onnx.SparseTensorProto.toObject(r.sparseTensors[m],a)}return l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.AttributeProto"},f.AttributeType=function(){var r={},a=Object.create(r);return a[r[0]="UNDEFINED"]=0,a[r[1]="FLOAT"]=1,a[r[2]="INT"]=2,a[r[3]="STRING"]=3,a[r[4]="TENSOR"]=4,a[r[5]="GRAPH"]=5,a[r[11]="SPARSE_TENSOR"]=11,a[r[13]="TYPE_PROTO"]=13,a[r[6]="FLOATS"]=6,a[r[7]="INTS"]=7,a[r[8]="STRINGS"]=8,a[r[9]="TENSORS"]=9,a[r[10]="GRAPHS"]=10,a[r[12]="SPARSE_TENSORS"]=12,a[r[14]="TYPE_PROTOS"]=14,a}(),f}(),d.ValueInfoProto=function(){function f(r){if(r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.name="",f.prototype.type=null,f.prototype.docString="",f.create=function(r){return new f(r)},f.encode=function(r,a){return a||(a=u.create()),r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(10).string(r.name),r.type!=null&&Object.hasOwnProperty.call(r,"type")&&s.onnx.TypeProto.encode(r.type,a.uint32(18).fork()).ldelim(),r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(26).string(r.docString),a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.ValueInfoProto;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{p.name=r.string();break}case 2:{p.type=s.onnx.TypeProto.decode(r,r.uint32());break}case 3:{p.docString=r.string();break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.type!=null&&r.hasOwnProperty("type")){var a=s.onnx.TypeProto.verify(r.type);if(a)return"type."+a}return r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString)?"docString: string expected":null},f.fromObject=function(r){if(r instanceof s.onnx.ValueInfoProto)return r;var a=new s.onnx.ValueInfoProto;if(r.name!=null&&(a.name=String(r.name)),r.type!=null){if(typeof r.type!="object")throw TypeError(".onnx.ValueInfoProto.type: object expected");a.type=s.onnx.TypeProto.fromObject(r.type)}return r.docString!=null&&(a.docString=String(r.docString)),a},f.toObject=function(r,a){a||(a={});var l={};return a.defaults&&(l.name="",l.type=null,l.docString=""),r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.type!=null&&r.hasOwnProperty("type")&&(l.type=s.onnx.TypeProto.toObject(r.type,a)),r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.ValueInfoProto"},f}(),d.NodeProto=function(){function f(r){if(this.input=[],this.output=[],this.attribute=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.input=n.emptyArray,f.prototype.output=n.emptyArray,f.prototype.name="",f.prototype.opType="",f.prototype.domain="",f.prototype.attribute=n.emptyArray,f.prototype.docString="",f.create=function(r){return new f(r)},f.encode=function(r,a){if(a||(a=u.create()),r.input!=null&&r.input.length)for(var l=0;l<r.input.length;++l)a.uint32(10).string(r.input[l]);if(r.output!=null&&r.output.length)for(var l=0;l<r.output.length;++l)a.uint32(18).string(r.output[l]);if(r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(26).string(r.name),r.opType!=null&&Object.hasOwnProperty.call(r,"opType")&&a.uint32(34).string(r.opType),r.attribute!=null&&r.attribute.length)for(var l=0;l<r.attribute.length;++l)s.onnx.AttributeProto.encode(r.attribute[l],a.uint32(42).fork()).ldelim();return r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(50).string(r.docString),r.domain!=null&&Object.hasOwnProperty.call(r,"domain")&&a.uint32(58).string(r.domain),a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.NodeProto;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{p.input&&p.input.length||(p.input=[]),p.input.push(r.string());break}case 2:{p.output&&p.output.length||(p.output=[]),p.output.push(r.string());break}case 3:{p.name=r.string();break}case 4:{p.opType=r.string();break}case 7:{p.domain=r.string();break}case 5:{p.attribute&&p.attribute.length||(p.attribute=[]),p.attribute.push(s.onnx.AttributeProto.decode(r,r.uint32()));break}case 6:{p.docString=r.string();break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.input!=null&&r.hasOwnProperty("input")){if(!Array.isArray(r.input))return"input: array expected";for(var a=0;a<r.input.length;++a)if(!n.isString(r.input[a]))return"input: string[] expected"}if(r.output!=null&&r.hasOwnProperty("output")){if(!Array.isArray(r.output))return"output: array expected";for(var a=0;a<r.output.length;++a)if(!n.isString(r.output[a]))return"output: string[] expected"}if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.opType!=null&&r.hasOwnProperty("opType")&&!n.isString(r.opType))return"opType: string expected";if(r.domain!=null&&r.hasOwnProperty("domain")&&!n.isString(r.domain))return"domain: string expected";if(r.attribute!=null&&r.hasOwnProperty("attribute")){if(!Array.isArray(r.attribute))return"attribute: array expected";for(var a=0;a<r.attribute.length;++a){var l=s.onnx.AttributeProto.verify(r.attribute[a]);if(l)return"attribute."+l}}return r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString)?"docString: string expected":null},f.fromObject=function(r){if(r instanceof s.onnx.NodeProto)return r;var a=new s.onnx.NodeProto;if(r.input){if(!Array.isArray(r.input))throw TypeError(".onnx.NodeProto.input: array expected");a.input=[];for(var l=0;l<r.input.length;++l)a.input[l]=String(r.input[l])}if(r.output){if(!Array.isArray(r.output))throw TypeError(".onnx.NodeProto.output: array expected");a.output=[];for(var l=0;l<r.output.length;++l)a.output[l]=String(r.output[l])}if(r.name!=null&&(a.name=String(r.name)),r.opType!=null&&(a.opType=String(r.opType)),r.domain!=null&&(a.domain=String(r.domain)),r.attribute){if(!Array.isArray(r.attribute))throw TypeError(".onnx.NodeProto.attribute: array expected");a.attribute=[];for(var l=0;l<r.attribute.length;++l){if(typeof r.attribute[l]!="object")throw TypeError(".onnx.NodeProto.attribute: object expected");a.attribute[l]=s.onnx.AttributeProto.fromObject(r.attribute[l])}}return r.docString!=null&&(a.docString=String(r.docString)),a},f.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.input=[],l.output=[],l.attribute=[]),a.defaults&&(l.name="",l.opType="",l.docString="",l.domain=""),r.input&&r.input.length){l.input=[];for(var p=0;p<r.input.length;++p)l.input[p]=r.input[p]}if(r.output&&r.output.length){l.output=[];for(var p=0;p<r.output.length;++p)l.output[p]=r.output[p]}if(r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.opType!=null&&r.hasOwnProperty("opType")&&(l.opType=r.opType),r.attribute&&r.attribute.length){l.attribute=[];for(var p=0;p<r.attribute.length;++p)l.attribute[p]=s.onnx.AttributeProto.toObject(r.attribute[p],a)}return r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.domain!=null&&r.hasOwnProperty("domain")&&(l.domain=r.domain),l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.NodeProto"},f}(),d.TrainingInfoProto=function(){function f(r){if(this.initializationBinding=[],this.updateBinding=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.initialization=null,f.prototype.algorithm=null,f.prototype.initializationBinding=n.emptyArray,f.prototype.updateBinding=n.emptyArray,f.create=function(r){return new f(r)},f.encode=function(r,a){if(a||(a=u.create()),r.initialization!=null&&Object.hasOwnProperty.call(r,"initialization")&&s.onnx.GraphProto.encode(r.initialization,a.uint32(10).fork()).ldelim(),r.algorithm!=null&&Object.hasOwnProperty.call(r,"algorithm")&&s.onnx.GraphProto.encode(r.algorithm,a.uint32(18).fork()).ldelim(),r.initializationBinding!=null&&r.initializationBinding.length)for(var l=0;l<r.initializationBinding.length;++l)s.onnx.StringStringEntryProto.encode(r.initializationBinding[l],a.uint32(26).fork()).ldelim();if(r.updateBinding!=null&&r.updateBinding.length)for(var l=0;l<r.updateBinding.length;++l)s.onnx.StringStringEntryProto.encode(r.updateBinding[l],a.uint32(34).fork()).ldelim();return a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.TrainingInfoProto;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{p.initialization=s.onnx.GraphProto.decode(r,r.uint32());break}case 2:{p.algorithm=s.onnx.GraphProto.decode(r,r.uint32());break}case 3:{p.initializationBinding&&p.initializationBinding.length||(p.initializationBinding=[]),p.initializationBinding.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}case 4:{p.updateBinding&&p.updateBinding.length||(p.updateBinding=[]),p.updateBinding.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.initialization!=null&&r.hasOwnProperty("initialization")){var a=s.onnx.GraphProto.verify(r.initialization);if(a)return"initialization."+a}if(r.algorithm!=null&&r.hasOwnProperty("algorithm")){var a=s.onnx.GraphProto.verify(r.algorithm);if(a)return"algorithm."+a}if(r.initializationBinding!=null&&r.hasOwnProperty("initializationBinding")){if(!Array.isArray(r.initializationBinding))return"initializationBinding: array expected";for(var l=0;l<r.initializationBinding.length;++l){var a=s.onnx.StringStringEntryProto.verify(r.initializationBinding[l]);if(a)return"initializationBinding."+a}}if(r.updateBinding!=null&&r.hasOwnProperty("updateBinding")){if(!Array.isArray(r.updateBinding))return"updateBinding: array expected";for(var l=0;l<r.updateBinding.length;++l){var a=s.onnx.StringStringEntryProto.verify(r.updateBinding[l]);if(a)return"updateBinding."+a}}return null},f.fromObject=function(r){if(r instanceof s.onnx.TrainingInfoProto)return r;var a=new s.onnx.TrainingInfoProto;if(r.initialization!=null){if(typeof r.initialization!="object")throw TypeError(".onnx.TrainingInfoProto.initialization: object expected");a.initialization=s.onnx.GraphProto.fromObject(r.initialization)}if(r.algorithm!=null){if(typeof r.algorithm!="object")throw TypeError(".onnx.TrainingInfoProto.algorithm: object expected");a.algorithm=s.onnx.GraphProto.fromObject(r.algorithm)}if(r.initializationBinding){if(!Array.isArray(r.initializationBinding))throw TypeError(".onnx.TrainingInfoProto.initializationBinding: array expected");a.initializationBinding=[];for(var l=0;l<r.initializationBinding.length;++l){if(typeof r.initializationBinding[l]!="object")throw TypeError(".onnx.TrainingInfoProto.initializationBinding: object expected");a.initializationBinding[l]=s.onnx.StringStringEntryProto.fromObject(r.initializationBinding[l])}}if(r.updateBinding){if(!Array.isArray(r.updateBinding))throw TypeError(".onnx.TrainingInfoProto.updateBinding: array expected");a.updateBinding=[];for(var l=0;l<r.updateBinding.length;++l){if(typeof r.updateBinding[l]!="object")throw TypeError(".onnx.TrainingInfoProto.updateBinding: object expected");a.updateBinding[l]=s.onnx.StringStringEntryProto.fromObject(r.updateBinding[l])}}return a},f.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.initializationBinding=[],l.updateBinding=[]),a.defaults&&(l.initialization=null,l.algorithm=null),r.initialization!=null&&r.hasOwnProperty("initialization")&&(l.initialization=s.onnx.GraphProto.toObject(r.initialization,a)),r.algorithm!=null&&r.hasOwnProperty("algorithm")&&(l.algorithm=s.onnx.GraphProto.toObject(r.algorithm,a)),r.initializationBinding&&r.initializationBinding.length){l.initializationBinding=[];for(var p=0;p<r.initializationBinding.length;++p)l.initializationBinding[p]=s.onnx.StringStringEntryProto.toObject(r.initializationBinding[p],a)}if(r.updateBinding&&r.updateBinding.length){l.updateBinding=[];for(var p=0;p<r.updateBinding.length;++p)l.updateBinding[p]=s.onnx.StringStringEntryProto.toObject(r.updateBinding[p],a)}return l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.TrainingInfoProto"},f}(),d.ModelProto=function(){function f(r){if(this.opsetImport=[],this.metadataProps=[],this.trainingInfo=[],this.functions=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.irVersion=n.Long?n.Long.fromBits(0,0,!1):0,f.prototype.opsetImport=n.emptyArray,f.prototype.producerName="",f.prototype.producerVersion="",f.prototype.domain="",f.prototype.modelVersion=n.Long?n.Long.fromBits(0,0,!1):0,f.prototype.docString="",f.prototype.graph=null,f.prototype.metadataProps=n.emptyArray,f.prototype.trainingInfo=n.emptyArray,f.prototype.functions=n.emptyArray,f.create=function(r){return new f(r)},f.encode=function(r,a){if(a||(a=u.create()),r.irVersion!=null&&Object.hasOwnProperty.call(r,"irVersion")&&a.uint32(8).int64(r.irVersion),r.producerName!=null&&Object.hasOwnProperty.call(r,"producerName")&&a.uint32(18).string(r.producerName),r.producerVersion!=null&&Object.hasOwnProperty.call(r,"producerVersion")&&a.uint32(26).string(r.producerVersion),r.domain!=null&&Object.hasOwnProperty.call(r,"domain")&&a.uint32(34).string(r.domain),r.modelVersion!=null&&Object.hasOwnProperty.call(r,"modelVersion")&&a.uint32(40).int64(r.modelVersion),r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(50).string(r.docString),r.graph!=null&&Object.hasOwnProperty.call(r,"graph")&&s.onnx.GraphProto.encode(r.graph,a.uint32(58).fork()).ldelim(),r.opsetImport!=null&&r.opsetImport.length)for(var l=0;l<r.opsetImport.length;++l)s.onnx.OperatorSetIdProto.encode(r.opsetImport[l],a.uint32(66).fork()).ldelim();if(r.metadataProps!=null&&r.metadataProps.length)for(var l=0;l<r.metadataProps.length;++l)s.onnx.StringStringEntryProto.encode(r.metadataProps[l],a.uint32(114).fork()).ldelim();if(r.trainingInfo!=null&&r.trainingInfo.length)for(var l=0;l<r.trainingInfo.length;++l)s.onnx.TrainingInfoProto.encode(r.trainingInfo[l],a.uint32(162).fork()).ldelim();if(r.functions!=null&&r.functions.length)for(var l=0;l<r.functions.length;++l)s.onnx.FunctionProto.encode(r.functions[l],a.uint32(202).fork()).ldelim();return a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.ModelProto;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{p.irVersion=r.int64();break}case 8:{p.opsetImport&&p.opsetImport.length||(p.opsetImport=[]),p.opsetImport.push(s.onnx.OperatorSetIdProto.decode(r,r.uint32()));break}case 2:{p.producerName=r.string();break}case 3:{p.producerVersion=r.string();break}case 4:{p.domain=r.string();break}case 5:{p.modelVersion=r.int64();break}case 6:{p.docString=r.string();break}case 7:{p.graph=s.onnx.GraphProto.decode(r,r.uint32());break}case 14:{p.metadataProps&&p.metadataProps.length||(p.metadataProps=[]),p.metadataProps.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}case 20:{p.trainingInfo&&p.trainingInfo.length||(p.trainingInfo=[]),p.trainingInfo.push(s.onnx.TrainingInfoProto.decode(r,r.uint32()));break}case 25:{p.functions&&p.functions.length||(p.functions=[]),p.functions.push(s.onnx.FunctionProto.decode(r,r.uint32()));break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.irVersion!=null&&r.hasOwnProperty("irVersion")&&!n.isInteger(r.irVersion)&&!(r.irVersion&&n.isInteger(r.irVersion.low)&&n.isInteger(r.irVersion.high)))return"irVersion: integer|Long expected";if(r.opsetImport!=null&&r.hasOwnProperty("opsetImport")){if(!Array.isArray(r.opsetImport))return"opsetImport: array expected";for(var a=0;a<r.opsetImport.length;++a){var l=s.onnx.OperatorSetIdProto.verify(r.opsetImport[a]);if(l)return"opsetImport."+l}}if(r.producerName!=null&&r.hasOwnProperty("producerName")&&!n.isString(r.producerName))return"producerName: string expected";if(r.producerVersion!=null&&r.hasOwnProperty("producerVersion")&&!n.isString(r.producerVersion))return"producerVersion: string expected";if(r.domain!=null&&r.hasOwnProperty("domain")&&!n.isString(r.domain))return"domain: string expected";if(r.modelVersion!=null&&r.hasOwnProperty("modelVersion")&&!n.isInteger(r.modelVersion)&&!(r.modelVersion&&n.isInteger(r.modelVersion.low)&&n.isInteger(r.modelVersion.high)))return"modelVersion: integer|Long expected";if(r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString))return"docString: string expected";if(r.graph!=null&&r.hasOwnProperty("graph")){var l=s.onnx.GraphProto.verify(r.graph);if(l)return"graph."+l}if(r.metadataProps!=null&&r.hasOwnProperty("metadataProps")){if(!Array.isArray(r.metadataProps))return"metadataProps: array expected";for(var a=0;a<r.metadataProps.length;++a){var l=s.onnx.StringStringEntryProto.verify(r.metadataProps[a]);if(l)return"metadataProps."+l}}if(r.trainingInfo!=null&&r.hasOwnProperty("trainingInfo")){if(!Array.isArray(r.trainingInfo))return"trainingInfo: array expected";for(var a=0;a<r.trainingInfo.length;++a){var l=s.onnx.TrainingInfoProto.verify(r.trainingInfo[a]);if(l)return"trainingInfo."+l}}if(r.functions!=null&&r.hasOwnProperty("functions")){if(!Array.isArray(r.functions))return"functions: array expected";for(var a=0;a<r.functions.length;++a){var l=s.onnx.FunctionProto.verify(r.functions[a]);if(l)return"functions."+l}}return null},f.fromObject=function(r){if(r instanceof s.onnx.ModelProto)return r;var a=new s.onnx.ModelProto;if(r.irVersion!=null&&(n.Long?(a.irVersion=n.Long.fromValue(r.irVersion)).unsigned=!1:typeof r.irVersion=="string"?a.irVersion=parseInt(r.irVersion,10):typeof r.irVersion=="number"?a.irVersion=r.irVersion:typeof r.irVersion=="object"&&(a.irVersion=new n.LongBits(r.irVersion.low>>>0,r.irVersion.high>>>0).toNumber())),r.opsetImport){if(!Array.isArray(r.opsetImport))throw TypeError(".onnx.ModelProto.opsetImport: array expected");a.opsetImport=[];for(var l=0;l<r.opsetImport.length;++l){if(typeof r.opsetImport[l]!="object")throw TypeError(".onnx.ModelProto.opsetImport: object expected");a.opsetImport[l]=s.onnx.OperatorSetIdProto.fromObject(r.opsetImport[l])}}if(r.producerName!=null&&(a.producerName=String(r.producerName)),r.producerVersion!=null&&(a.producerVersion=String(r.producerVersion)),r.domain!=null&&(a.domain=String(r.domain)),r.modelVersion!=null&&(n.Long?(a.modelVersion=n.Long.fromValue(r.modelVersion)).unsigned=!1:typeof r.modelVersion=="string"?a.modelVersion=parseInt(r.modelVersion,10):typeof r.modelVersion=="number"?a.modelVersion=r.modelVersion:typeof r.modelVersion=="object"&&(a.modelVersion=new n.LongBits(r.modelVersion.low>>>0,r.modelVersion.high>>>0).toNumber())),r.docString!=null&&(a.docString=String(r.docString)),r.graph!=null){if(typeof r.graph!="object")throw TypeError(".onnx.ModelProto.graph: object expected");a.graph=s.onnx.GraphProto.fromObject(r.graph)}if(r.metadataProps){if(!Array.isArray(r.metadataProps))throw TypeError(".onnx.ModelProto.metadataProps: array expected");a.metadataProps=[];for(var l=0;l<r.metadataProps.length;++l){if(typeof r.metadataProps[l]!="object")throw TypeError(".onnx.ModelProto.metadataProps: object expected");a.metadataProps[l]=s.onnx.StringStringEntryProto.fromObject(r.metadataProps[l])}}if(r.trainingInfo){if(!Array.isArray(r.trainingInfo))throw TypeError(".onnx.ModelProto.trainingInfo: array expected");a.trainingInfo=[];for(var l=0;l<r.trainingInfo.length;++l){if(typeof r.trainingInfo[l]!="object")throw TypeError(".onnx.ModelProto.trainingInfo: object expected");a.trainingInfo[l]=s.onnx.TrainingInfoProto.fromObject(r.trainingInfo[l])}}if(r.functions){if(!Array.isArray(r.functions))throw TypeError(".onnx.ModelProto.functions: array expected");a.functions=[];for(var l=0;l<r.functions.length;++l){if(typeof r.functions[l]!="object")throw TypeError(".onnx.ModelProto.functions: object expected");a.functions[l]=s.onnx.FunctionProto.fromObject(r.functions[l])}}return a},f.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.opsetImport=[],l.metadataProps=[],l.trainingInfo=[],l.functions=[]),a.defaults){if(n.Long){var p=new n.Long(0,0,!1);l.irVersion=a.longs===String?p.toString():a.longs===Number?p.toNumber():p}else l.irVersion=a.longs===String?"0":0;if(l.producerName="",l.producerVersion="",l.domain="",n.Long){var p=new n.Long(0,0,!1);l.modelVersion=a.longs===String?p.toString():a.longs===Number?p.toNumber():p}else l.modelVersion=a.longs===String?"0":0;l.docString="",l.graph=null}if(r.irVersion!=null&&r.hasOwnProperty("irVersion")&&(typeof r.irVersion=="number"?l.irVersion=a.longs===String?String(r.irVersion):r.irVersion:l.irVersion=a.longs===String?n.Long.prototype.toString.call(r.irVersion):a.longs===Number?new n.LongBits(r.irVersion.low>>>0,r.irVersion.high>>>0).toNumber():r.irVersion),r.producerName!=null&&r.hasOwnProperty("producerName")&&(l.producerName=r.producerName),r.producerVersion!=null&&r.hasOwnProperty("producerVersion")&&(l.producerVersion=r.producerVersion),r.domain!=null&&r.hasOwnProperty("domain")&&(l.domain=r.domain),r.modelVersion!=null&&r.hasOwnProperty("modelVersion")&&(typeof r.modelVersion=="number"?l.modelVersion=a.longs===String?String(r.modelVersion):r.modelVersion:l.modelVersion=a.longs===String?n.Long.prototype.toString.call(r.modelVersion):a.longs===Number?new n.LongBits(r.modelVersion.low>>>0,r.modelVersion.high>>>0).toNumber():r.modelVersion),r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.graph!=null&&r.hasOwnProperty("graph")&&(l.graph=s.onnx.GraphProto.toObject(r.graph,a)),r.opsetImport&&r.opsetImport.length){l.opsetImport=[];for(var m=0;m<r.opsetImport.length;++m)l.opsetImport[m]=s.onnx.OperatorSetIdProto.toObject(r.opsetImport[m],a)}if(r.metadataProps&&r.metadataProps.length){l.metadataProps=[];for(var m=0;m<r.metadataProps.length;++m)l.metadataProps[m]=s.onnx.StringStringEntryProto.toObject(r.metadataProps[m],a)}if(r.trainingInfo&&r.trainingInfo.length){l.trainingInfo=[];for(var m=0;m<r.trainingInfo.length;++m)l.trainingInfo[m]=s.onnx.TrainingInfoProto.toObject(r.trainingInfo[m],a)}if(r.functions&&r.functions.length){l.functions=[];for(var m=0;m<r.functions.length;++m)l.functions[m]=s.onnx.FunctionProto.toObject(r.functions[m],a)}return l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.ModelProto"},f}(),d.StringStringEntryProto=function(){function f(r){if(r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.key="",f.prototype.value="",f.create=function(r){return new f(r)},f.encode=function(r,a){return a||(a=u.create()),r.key!=null&&Object.hasOwnProperty.call(r,"key")&&a.uint32(10).string(r.key),r.value!=null&&Object.hasOwnProperty.call(r,"value")&&a.uint32(18).string(r.value),a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.StringStringEntryProto;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{p.key=r.string();break}case 2:{p.value=r.string();break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){return typeof r!="object"||r===null?"object expected":r.key!=null&&r.hasOwnProperty("key")&&!n.isString(r.key)?"key: string expected":r.value!=null&&r.hasOwnProperty("value")&&!n.isString(r.value)?"value: string expected":null},f.fromObject=function(r){if(r instanceof s.onnx.StringStringEntryProto)return r;var a=new s.onnx.StringStringEntryProto;return r.key!=null&&(a.key=String(r.key)),r.value!=null&&(a.value=String(r.value)),a},f.toObject=function(r,a){a||(a={});var l={};return a.defaults&&(l.key="",l.value=""),r.key!=null&&r.hasOwnProperty("key")&&(l.key=r.key),r.value!=null&&r.hasOwnProperty("value")&&(l.value=r.value),l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.StringStringEntryProto"},f}(),d.TensorAnnotation=function(){function f(r){if(this.quantParameterTensorNames=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.tensorName="",f.prototype.quantParameterTensorNames=n.emptyArray,f.create=function(r){return new f(r)},f.encode=function(r,a){if(a||(a=u.create()),r.tensorName!=null&&Object.hasOwnProperty.call(r,"tensorName")&&a.uint32(10).string(r.tensorName),r.quantParameterTensorNames!=null&&r.quantParameterTensorNames.length)for(var l=0;l<r.quantParameterTensorNames.length;++l)s.onnx.StringStringEntryProto.encode(r.quantParameterTensorNames[l],a.uint32(18).fork()).ldelim();return a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.TensorAnnotation;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{p.tensorName=r.string();break}case 2:{p.quantParameterTensorNames&&p.quantParameterTensorNames.length||(p.quantParameterTensorNames=[]),p.quantParameterTensorNames.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.tensorName!=null&&r.hasOwnProperty("tensorName")&&!n.isString(r.tensorName))return"tensorName: string expected";if(r.quantParameterTensorNames!=null&&r.hasOwnProperty("quantParameterTensorNames")){if(!Array.isArray(r.quantParameterTensorNames))return"quantParameterTensorNames: array expected";for(var a=0;a<r.quantParameterTensorNames.length;++a){var l=s.onnx.StringStringEntryProto.verify(r.quantParameterTensorNames[a]);if(l)return"quantParameterTensorNames."+l}}return null},f.fromObject=function(r){if(r instanceof s.onnx.TensorAnnotation)return r;var a=new s.onnx.TensorAnnotation;if(r.tensorName!=null&&(a.tensorName=String(r.tensorName)),r.quantParameterTensorNames){if(!Array.isArray(r.quantParameterTensorNames))throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");a.quantParameterTensorNames=[];for(var l=0;l<r.quantParameterTensorNames.length;++l){if(typeof r.quantParameterTensorNames[l]!="object")throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");a.quantParameterTensorNames[l]=s.onnx.StringStringEntryProto.fromObject(r.quantParameterTensorNames[l])}}return a},f.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.quantParameterTensorNames=[]),a.defaults&&(l.tensorName=""),r.tensorName!=null&&r.hasOwnProperty("tensorName")&&(l.tensorName=r.tensorName),r.quantParameterTensorNames&&r.quantParameterTensorNames.length){l.quantParameterTensorNames=[];for(var p=0;p<r.quantParameterTensorNames.length;++p)l.quantParameterTensorNames[p]=s.onnx.StringStringEntryProto.toObject(r.quantParameterTensorNames[p],a)}return l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.TensorAnnotation"},f}(),d.GraphProto=function(){function f(r){if(this.node=[],this.initializer=[],this.sparseInitializer=[],this.input=[],this.output=[],this.valueInfo=[],this.quantizationAnnotation=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.node=n.emptyArray,f.prototype.name="",f.prototype.initializer=n.emptyArray,f.prototype.sparseInitializer=n.emptyArray,f.prototype.docString="",f.prototype.input=n.emptyArray,f.prototype.output=n.emptyArray,f.prototype.valueInfo=n.emptyArray,f.prototype.quantizationAnnotation=n.emptyArray,f.create=function(r){return new f(r)},f.encode=function(r,a){if(a||(a=u.create()),r.node!=null&&r.node.length)for(var l=0;l<r.node.length;++l)s.onnx.NodeProto.encode(r.node[l],a.uint32(10).fork()).ldelim();if(r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(18).string(r.name),r.initializer!=null&&r.initializer.length)for(var l=0;l<r.initializer.length;++l)s.onnx.TensorProto.encode(r.initializer[l],a.uint32(42).fork()).ldelim();if(r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(82).string(r.docString),r.input!=null&&r.input.length)for(var l=0;l<r.input.length;++l)s.onnx.ValueInfoProto.encode(r.input[l],a.uint32(90).fork()).ldelim();if(r.output!=null&&r.output.length)for(var l=0;l<r.output.length;++l)s.onnx.ValueInfoProto.encode(r.output[l],a.uint32(98).fork()).ldelim();if(r.valueInfo!=null&&r.valueInfo.length)for(var l=0;l<r.valueInfo.length;++l)s.onnx.ValueInfoProto.encode(r.valueInfo[l],a.uint32(106).fork()).ldelim();if(r.quantizationAnnotation!=null&&r.quantizationAnnotation.length)for(var l=0;l<r.quantizationAnnotation.length;++l)s.onnx.TensorAnnotation.encode(r.quantizationAnnotation[l],a.uint32(114).fork()).ldelim();if(r.sparseInitializer!=null&&r.sparseInitializer.length)for(var l=0;l<r.sparseInitializer.length;++l)s.onnx.SparseTensorProto.encode(r.sparseInitializer[l],a.uint32(122).fork()).ldelim();return a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.GraphProto;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{p.node&&p.node.length||(p.node=[]),p.node.push(s.onnx.NodeProto.decode(r,r.uint32()));break}case 2:{p.name=r.string();break}case 5:{p.initializer&&p.initializer.length||(p.initializer=[]),p.initializer.push(s.onnx.TensorProto.decode(r,r.uint32()));break}case 15:{p.sparseInitializer&&p.sparseInitializer.length||(p.sparseInitializer=[]),p.sparseInitializer.push(s.onnx.SparseTensorProto.decode(r,r.uint32()));break}case 10:{p.docString=r.string();break}case 11:{p.input&&p.input.length||(p.input=[]),p.input.push(s.onnx.ValueInfoProto.decode(r,r.uint32()));break}case 12:{p.output&&p.output.length||(p.output=[]),p.output.push(s.onnx.ValueInfoProto.decode(r,r.uint32()));break}case 13:{p.valueInfo&&p.valueInfo.length||(p.valueInfo=[]),p.valueInfo.push(s.onnx.ValueInfoProto.decode(r,r.uint32()));break}case 14:{p.quantizationAnnotation&&p.quantizationAnnotation.length||(p.quantizationAnnotation=[]),p.quantizationAnnotation.push(s.onnx.TensorAnnotation.decode(r,r.uint32()));break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.node!=null&&r.hasOwnProperty("node")){if(!Array.isArray(r.node))return"node: array expected";for(var a=0;a<r.node.length;++a){var l=s.onnx.NodeProto.verify(r.node[a]);if(l)return"node."+l}}if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.initializer!=null&&r.hasOwnProperty("initializer")){if(!Array.isArray(r.initializer))return"initializer: array expected";for(var a=0;a<r.initializer.length;++a){var l=s.onnx.TensorProto.verify(r.initializer[a]);if(l)return"initializer."+l}}if(r.sparseInitializer!=null&&r.hasOwnProperty("sparseInitializer")){if(!Array.isArray(r.sparseInitializer))return"sparseInitializer: array expected";for(var a=0;a<r.sparseInitializer.length;++a){var l=s.onnx.SparseTensorProto.verify(r.sparseInitializer[a]);if(l)return"sparseInitializer."+l}}if(r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString))return"docString: string expected";if(r.input!=null&&r.hasOwnProperty("input")){if(!Array.isArray(r.input))return"input: array expected";for(var a=0;a<r.input.length;++a){var l=s.onnx.ValueInfoProto.verify(r.input[a]);if(l)return"input."+l}}if(r.output!=null&&r.hasOwnProperty("output")){if(!Array.isArray(r.output))return"output: array expected";for(var a=0;a<r.output.length;++a){var l=s.onnx.ValueInfoProto.verify(r.output[a]);if(l)return"output."+l}}if(r.valueInfo!=null&&r.hasOwnProperty("valueInfo")){if(!Array.isArray(r.valueInfo))return"valueInfo: array expected";for(var a=0;a<r.valueInfo.length;++a){var l=s.onnx.ValueInfoProto.verify(r.valueInfo[a]);if(l)return"valueInfo."+l}}if(r.quantizationAnnotation!=null&&r.hasOwnProperty("quantizationAnnotation")){if(!Array.isArray(r.quantizationAnnotation))return"quantizationAnnotation: array expected";for(var a=0;a<r.quantizationAnnotation.length;++a){var l=s.onnx.TensorAnnotation.verify(r.quantizationAnnotation[a]);if(l)return"quantizationAnnotation."+l}}return null},f.fromObject=function(r){if(r instanceof s.onnx.GraphProto)return r;var a=new s.onnx.GraphProto;if(r.node){if(!Array.isArray(r.node))throw TypeError(".onnx.GraphProto.node: array expected");a.node=[];for(var l=0;l<r.node.length;++l){if(typeof r.node[l]!="object")throw TypeError(".onnx.GraphProto.node: object expected");a.node[l]=s.onnx.NodeProto.fromObject(r.node[l])}}if(r.name!=null&&(a.name=String(r.name)),r.initializer){if(!Array.isArray(r.initializer))throw TypeError(".onnx.GraphProto.initializer: array expected");a.initializer=[];for(var l=0;l<r.initializer.length;++l){if(typeof r.initializer[l]!="object")throw TypeError(".onnx.GraphProto.initializer: object expected");a.initializer[l]=s.onnx.TensorProto.fromObject(r.initializer[l])}}if(r.sparseInitializer){if(!Array.isArray(r.sparseInitializer))throw TypeError(".onnx.GraphProto.sparseInitializer: array expected");a.sparseInitializer=[];for(var l=0;l<r.sparseInitializer.length;++l){if(typeof r.sparseInitializer[l]!="object")throw TypeError(".onnx.GraphProto.sparseInitializer: object expected");a.sparseInitializer[l]=s.onnx.SparseTensorProto.fromObject(r.sparseInitializer[l])}}if(r.docString!=null&&(a.docString=String(r.docString)),r.input){if(!Array.isArray(r.input))throw TypeError(".onnx.GraphProto.input: array expected");a.input=[];for(var l=0;l<r.input.length;++l){if(typeof r.input[l]!="object")throw TypeError(".onnx.GraphProto.input: object expected");a.input[l]=s.onnx.ValueInfoProto.fromObject(r.input[l])}}if(r.output){if(!Array.isArray(r.output))throw TypeError(".onnx.GraphProto.output: array expected");a.output=[];for(var l=0;l<r.output.length;++l){if(typeof r.output[l]!="object")throw TypeError(".onnx.GraphProto.output: object expected");a.output[l]=s.onnx.ValueInfoProto.fromObject(r.output[l])}}if(r.valueInfo){if(!Array.isArray(r.valueInfo))throw TypeError(".onnx.GraphProto.valueInfo: array expected");a.valueInfo=[];for(var l=0;l<r.valueInfo.length;++l){if(typeof r.valueInfo[l]!="object")throw TypeError(".onnx.GraphProto.valueInfo: object expected");a.valueInfo[l]=s.onnx.ValueInfoProto.fromObject(r.valueInfo[l])}}if(r.quantizationAnnotation){if(!Array.isArray(r.quantizationAnnotation))throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");a.quantizationAnnotation=[];for(var l=0;l<r.quantizationAnnotation.length;++l){if(typeof r.quantizationAnnotation[l]!="object")throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");a.quantizationAnnotation[l]=s.onnx.TensorAnnotation.fromObject(r.quantizationAnnotation[l])}}return a},f.toObject=function(r,a){a||(a={});var l={};if((a.arrays||a.defaults)&&(l.node=[],l.initializer=[],l.input=[],l.output=[],l.valueInfo=[],l.quantizationAnnotation=[],l.sparseInitializer=[]),a.defaults&&(l.name="",l.docString=""),r.node&&r.node.length){l.node=[];for(var p=0;p<r.node.length;++p)l.node[p]=s.onnx.NodeProto.toObject(r.node[p],a)}if(r.name!=null&&r.hasOwnProperty("name")&&(l.name=r.name),r.initializer&&r.initializer.length){l.initializer=[];for(var p=0;p<r.initializer.length;++p)l.initializer[p]=s.onnx.TensorProto.toObject(r.initializer[p],a)}if(r.docString!=null&&r.hasOwnProperty("docString")&&(l.docString=r.docString),r.input&&r.input.length){l.input=[];for(var p=0;p<r.input.length;++p)l.input[p]=s.onnx.ValueInfoProto.toObject(r.input[p],a)}if(r.output&&r.output.length){l.output=[];for(var p=0;p<r.output.length;++p)l.output[p]=s.onnx.ValueInfoProto.toObject(r.output[p],a)}if(r.valueInfo&&r.valueInfo.length){l.valueInfo=[];for(var p=0;p<r.valueInfo.length;++p)l.valueInfo[p]=s.onnx.ValueInfoProto.toObject(r.valueInfo[p],a)}if(r.quantizationAnnotation&&r.quantizationAnnotation.length){l.quantizationAnnotation=[];for(var p=0;p<r.quantizationAnnotation.length;++p)l.quantizationAnnotation[p]=s.onnx.TensorAnnotation.toObject(r.quantizationAnnotation[p],a)}if(r.sparseInitializer&&r.sparseInitializer.length){l.sparseInitializer=[];for(var p=0;p<r.sparseInitializer.length;++p)l.sparseInitializer[p]=s.onnx.SparseTensorProto.toObject(r.sparseInitializer[p],a)}return l},f.prototype.toJSON=function(){return this.constructor.toObject(this,o.util.toJSONOptions)},f.getTypeUrl=function(r){return r===void 0&&(r="type.googleapis.com"),r+"/onnx.GraphProto"},f}(),d.TensorProto=function(){function f(r){if(this.dims=[],this.floatData=[],this.int32Data=[],this.stringData=[],this.int64Data=[],this.externalData=[],this.doubleData=[],this.uint64Data=[],r)for(var a=Object.keys(r),l=0;l<a.length;++l)r[a[l]]!=null&&(this[a[l]]=r[a[l]])}return f.prototype.dims=n.emptyArray,f.prototype.dataType=0,f.prototype.segment=null,f.prototype.floatData=n.emptyArray,f.prototype.int32Data=n.emptyArray,f.prototype.stringData=n.emptyArray,f.prototype.int64Data=n.emptyArray,f.prototype.name="",f.prototype.docString="",f.prototype.rawData=n.newBuffer([]),f.prototype.externalData=n.emptyArray,f.prototype.dataLocation=0,f.prototype.doubleData=n.emptyArray,f.prototype.uint64Data=n.emptyArray,f.create=function(r){return new f(r)},f.encode=function(r,a){if(a||(a=u.create()),r.dims!=null&&r.dims.length){a.uint32(10).fork();for(var l=0;l<r.dims.length;++l)a.int64(r.dims[l]);a.ldelim()}if(r.dataType!=null&&Object.hasOwnProperty.call(r,"dataType")&&a.uint32(16).int32(r.dataType),r.segment!=null&&Object.hasOwnProperty.call(r,"segment")&&s.onnx.TensorProto.Segment.encode(r.segment,a.uint32(26).fork()).ldelim(),r.floatData!=null&&r.floatData.length){a.uint32(34).fork();for(var l=0;l<r.floatData.length;++l)a.float(r.floatData[l]);a.ldelim()}if(r.int32Data!=null&&r.int32Data.length){a.uint32(42).fork();for(var l=0;l<r.int32Data.length;++l)a.int32(r.int32Data[l]);a.ldelim()}if(r.stringData!=null&&r.stringData.length)for(var l=0;l<r.stringData.length;++l)a.uint32(50).bytes(r.stringData[l]);if(r.int64Data!=null&&r.int64Data.length){a.uint32(58).fork();for(var l=0;l<r.int64Data.length;++l)a.int64(r.int64Data[l]);a.ldelim()}if(r.name!=null&&Object.hasOwnProperty.call(r,"name")&&a.uint32(66).string(r.name),r.rawData!=null&&Object.hasOwnProperty.call(r,"rawData")&&a.uint32(74).bytes(r.rawData),r.doubleData!=null&&r.doubleData.length){a.uint32(82).fork();for(var l=0;l<r.doubleData.length;++l)a.double(r.doubleData[l]);a.ldelim()}if(r.uint64Data!=null&&r.uint64Data.length){a.uint32(90).fork();for(var l=0;l<r.uint64Data.length;++l)a.uint64(r.uint64Data[l]);a.ldelim()}if(r.docString!=null&&Object.hasOwnProperty.call(r,"docString")&&a.uint32(98).string(r.docString),r.externalData!=null&&r.externalData.length)for(var l=0;l<r.externalData.length;++l)s.onnx.StringStringEntryProto.encode(r.externalData[l],a.uint32(106).fork()).ldelim();return r.dataLocation!=null&&Object.hasOwnProperty.call(r,"dataLocation")&&a.uint32(112).int32(r.dataLocation),a},f.encodeDelimited=function(r,a){return this.encode(r,a).ldelim()},f.decode=function(r,a){r instanceof i||(r=i.create(r));for(var l=a===void 0?r.len:r.pos+a,p=new s.onnx.TensorProto;r.pos<l;){var m=r.uint32();switch(m>>>3){case 1:{if(p.dims&&p.dims.length||(p.dims=[]),(m&7)===2)for(var y=r.uint32()+r.pos;r.pos<y;)p.dims.push(r.int64());else p.dims.push(r.int64());break}case 2:{p.dataType=r.int32();break}case 3:{p.segment=s.onnx.TensorProto.Segment.decode(r,r.uint32());break}case 4:{if(p.floatData&&p.floatData.length||(p.floatData=[]),(m&7)===2)for(var y=r.uint32()+r.pos;r.pos<y;)p.floatData.push(r.float());else p.floatData.push(r.float());break}case 5:{if(p.int32Data&&p.int32Data.length||(p.int32Data=[]),(m&7)===2)for(var y=r.uint32()+r.pos;r.pos<y;)p.int32Data.push(r.int32());else p.int32Data.push(r.int32());break}case 6:{p.stringData&&p.stringData.length||(p.stringData=[]),p.stringData.push(r.bytes());break}case 7:{if(p.int64Data&&p.int64Data.length||(p.int64Data=[]),(m&7)===2)for(var y=r.uint32()+r.pos;r.pos<y;)p.int64Data.push(r.int64());else p.int64Data.push(r.int64());break}case 8:{p.name=r.string();break}case 12:{p.docString=r.string();break}case 9:{p.rawData=r.bytes();break}case 13:{p.externalData&&p.externalData.length||(p.externalData=[]),p.externalData.push(s.onnx.StringStringEntryProto.decode(r,r.uint32()));break}case 14:{p.dataLocation=r.int32();break}case 10:{if(p.doubleData&&p.doubleData.length||(p.doubleData=[]),(m&7)===2)for(var y=r.uint32()+r.pos;r.pos<y;)p.doubleData.push(r.double());else p.doubleData.push(r.double());break}case 11:{if(p.uint64Data&&p.uint64Data.length||(p.uint64Data=[]),(m&7)===2)for(var y=r.uint32()+r.pos;r.pos<y;)p.uint64Data.push(r.uint64());else p.uint64Data.push(r.uint64());break}default:r.skipType(m&7);break}}return p},f.decodeDelimited=function(r){return r instanceof i||(r=new i(r)),this.decode(r,r.uint32())},f.verify=function(r){if(typeof r!="object"||r===null)return"object expected";if(r.dims!=null&&r.hasOwnProperty("dims")){if(!Array.isArray(r.dims))return"dims: array expected";for(var a=0;a<r.dims.length;++a)if(!n.isInteger(r.dims[a])&&!(r.dims[a]&&n.isInteger(r.dims[a].low)&&n.isInteger(r.dims[a].high)))return"dims: integer|Long[] expected"}if(r.dataType!=null&&r.hasOwnProperty("dataType")&&!n.isInteger(r.dataType))return"dataType: integer expected";if(r.segment!=null&&r.hasOwnProperty("segment")){var l=s.onnx.TensorProto.Segment.verify(r.segment);if(l)return"segment."+l}if(r.floatData!=null&&r.hasOwnProperty("floatData")){if(!Array.isArray(r.floatData))return"floatData: array expected";for(var a=0;a<r.floatData.length;++a)if(typeof r.floatData[a]!="number")return"floatData: number[] expected"}if(r.int32Data!=null&&r.hasOwnProperty("int32Data")){if(!Array.isArray(r.int32Data))return"int32Data: array expected";for(var a=0;a<r.int32Data.length;++a)if(!n.isInteger(r.int32Data[a]))return"int32Data: integer[] expected"}if(r.stringData!=null&&r.hasOwnProperty("stringData")){if(!Array.isArray(r.stringData))return"stringData: array expected";for(var a=0;a<r.stringData.length;++a)if(!(r.stringData[a]&&typeof r.stringData[a].length=="number"||n.isString(r.stringData[a])))return"stringData: buffer[] expected"}if(r.int64Data!=null&&r.hasOwnProperty("int64Data")){if(!Array.isArray(r.int64Data))return"int64Data: array expected";for(var a=0;a<r.int64Data.length;++a)if(!n.isInteger(r.int64Data[a])&&!(r.int64Data[a]&&n.isInteger(r.int64Data[a].low)&&n.isInteger(r.int64Data[a].high)))return"int64Data: integer|Long[] expected"}if(r.name!=null&&r.hasOwnProperty("name")&&!n.isString(r.name))return"name: string expected";if(r.docString!=null&&r.hasOwnProperty("docString")&&!n.isString(r.docString))return"docString: string expected";if(r.rawData!=null&&r.hasOwnProperty("rawData")&&!(r.rawData&&typeof r.rawData.length=="number"||n.isString(r.rawData)))return"rawData: buffer expected";if(r.externalData!=null&&r.hasOwnProperty("externalData")){if(!Array.isArray(r.externalData))return"externalData: array expected";for(var a=0;a<r.externalData.length;++a){var l=s.onnx.StringStringEntryProto.verify(r.externalData[a]);if(l)return"externalData."+l}}if(r.dataLocation!=null&&r.hasOwnProperty("dataLocation"))switch(r.dataLocation){default:return"dataLocation: enum value expected";case 0:case 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expected";if(r.input!=null&&r.hasOwnProperty("input")){if(!Array.isArray(r.input))return"input: array expected";for(var a=0;a<r.input.length;++a)if(!n.isString(r.input[a]))return"input: string[] expected"}if(r.output!=null&&r.hasOwnProperty("output")){if(!Array.isArray(r.output))return"output: array expected";for(var a=0;a<r.output.length;++a)if(!n.isString(r.output[a]))return"output: string[] expected"}if(r.attribute!=null&&r.hasOwnProperty("attribute")){if(!Array.isArray(r.attribute))return"attribute: array expected";for(var a=0;a<r.attribute.length;++a)if(!n.isString(r.attribute[a]))return"attribute: string[] expected"}if(r.attributeProto!=null&&r.hasOwnProperty("attributeProto")){if(!Array.isArray(r.attributeProto))return"attributeProto: array expected";for(var a=0;a<r.attributeProto.length;++a){var l=s.onnx.AttributeProto.verify(r.attributeProto[a]);if(l)return"attributeProto."+l}}if(r.node!=null&&r.hasOwnProperty("node")){if(!Array.isArray(r.node))return"node: array 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0&&(r="type.googleapis.com"),r+"/onnx.FunctionProto"},f}(),d}(),e.exports=s});function Cr(t,e){if(!t)throw new Error(typeof e=="string"?e:e())}function sn(t){return new TextDecoder().decode(t)}var ye,cr,hi,Xe,Xn,Ve,nt,U,an,pr,dr,hr,fe=L(()=>{Wn(),ei(),ye=Er($r()),mr(),cr=class{static arraysEqual(t,e){if(t.length!==e.length)return!1;for(let o=0;o<t.length;o++)if(t[o]!==e[o])return!1;return!0}},hi=class{static preprocessInputShapes(t,e){let o=t.length===1?[1,t[0]]:t,i=e.length===1?[e[0],1]:e;return[o,i]}static postprocessOutputShape(t,e,o){e===1&&t.splice(t.length-2,1),o===1&&t.pop()}static calcMatMulShape(t,e){return t[1]!==e[0]?void 0:[t[0],e[1]]}},Xe=class fr{static calcShape(e,o,i=!1){let u=e.length,n=o.length;if(u===0)return o;if(n===0)return e;let s=Math.max(e.length,o.length),d=new Array(s);if(i){if(u<2||n<2)return;let f=hi.calcMatMulShape([e[u-2],e[u-1]],[o[n-2],o[n-1]]);if(f===void 0)return;[d[s-2],d[s-1]]=f}for(let f=i?3:1;f<=s;f++){let 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i=e.length,u=[];for(let n=0;n<i;n++){let s=i-1-n,d=e[s]||1;(o[o.length-1-n]||1)>1&&d===1&&u.unshift(s)}return u}},Xn=class{static getShapeOfGemmResult(t,e,o,i,u){if(t.length!==2||o.length!==2)throw new Error("shape need to be of size 2");let n,s,d;e?(n=t[1],s=t[0]):(n=t[0],s=t[1]);let f=-1;if(i?(d=o[0],f=1):(d=o[1],f=0),o[f]!==s)throw new Error("dimension mismatch");if(n<=0||d<=0||s<=0)throw new Error("invalid shape specified");if(u&&!Xe.isValidBroadcast(u,[n,d]))throw new Error("gemm: invalid bias shape for broadcast");return[n,d,s]}},Ve=class Sn{static tensorDataTypeFromProto(e){switch(e){case ye.onnx.TensorProto.DataType.INT8:return"int8";case ye.onnx.TensorProto.DataType.UINT8:return"uint8";case ye.onnx.TensorProto.DataType.BOOL:return"bool";case ye.onnx.TensorProto.DataType.INT16:return"int16";case ye.onnx.TensorProto.DataType.UINT16:return"uint16";case ye.onnx.TensorProto.DataType.INT32:return"int32";case ye.onnx.TensorProto.DataType.UINT32:return"uint32";case ye.onnx.TensorProto.DataType.FLOAT:return"float32";case ye.onnx.TensorProto.DataType.DOUBLE:return"float64";case ye.onnx.TensorProto.DataType.STRING:return"string";case ye.onnx.TensorProto.DataType.INT64:return"int32";case ye.onnx.TensorProto.DataType.UINT64:return"uint32";default:throw new Error(`unsupported data type: ${ye.onnx.TensorProto.DataType[e]}`)}}static tensorDataTypeStringToEnum(e){switch(e){case"int8":return ye.onnx.TensorProto.DataType.INT8;case"uint8":return ye.onnx.TensorProto.DataType.UINT8;case"bool":return ye.onnx.TensorProto.DataType.BOOL;case"int16":return ye.onnx.TensorProto.DataType.INT16;case"uint16":return ye.onnx.TensorProto.DataType.UINT16;case"int32":return ye.onnx.TensorProto.DataType.INT32;case"uint32":return ye.onnx.TensorProto.DataType.UINT32;case"float32":return ye.onnx.TensorProto.DataType.FLOAT;case"float64":return ye.onnx.TensorProto.DataType.DOUBLE;case"string":return ye.onnx.TensorProto.DataType.STRING;case"int64":return ye.onnx.TensorProto.DataType.INT64;case"uint64":return ye.onnx.TensorProto.DataType.UINT64;default:throw new Error(`unsupported data type: ${e}`)}}static tensorDimsFromProto(e){return e.map(o=>zt.isLong(o)?o.toNumber():o)}static tensorValueTypeFromProto(e){return{tensorType:Sn.tensorDataTypeFromProto(e.elemType),shape:{dims:Sn.tensorDimsFromProto(e.shape.dim.map(o=>o.dimValue))}}}static tensorDimsFromORTFormat(e){let o=[];for(let i=0;i<e.dimsLength();i++)o.push(nt.longToNumber(e.dims(i)));return o}static tensorAttributesFromORTFormat(e){let o=[];for(let i=0;i<e.attributesLength();i++)o.push(e.attributes(i));return o}},nt=class{static longToNumber(t,e){return zt.isLong(t)?t.toNumber():t instanceof _.Long?zt.fromValue({low:t.low,high:t.high,unsigned:e??!1}).toNumber():t}static isLong(t){return zt.isLong(t)||t instanceof _.Long}},U=class Pt{static size(e){return Pt.getSizeFromDimensionRange(e,0,e.length)}static sizeFromDimension(e,o){if(o<0||o>e.length)throw new Error(`invalid dimension 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different than 1");(o.length===0&&e[u]>1||o.length>0&&!n)&&i.push(e[u])}return i}static unsqueezeShape(e,o){let i=new Array(e.length+o.length);i.fill(0);for(let n=0;n<o.length;n++){let s=Pt.normalizeAxis(o[n],i.length);if(s>=i.length)throw new Error("'axes' has an out of range axis");if(i[s]!==0)throw new Error("'axes' has a duplicate axis");i[s]=1}let u=0;for(let n=0;n<i.length;n++)i[n]===0&&(i[n]=e[u++]);if(u!==e.length)throw new Error("the unsqueezed dimension could not be established");return i}},an=class js{static splitShape(e,o,i,u){if(i.length===0){if(!u)throw new Error("need to know number of outputs when the 'split' attribute is not specified");js.determineSplit(e[o],u,i)}let n=[],s=[0];for(let d=0;d<i.length;++d){d!==0&&s.push(s[d-1]+i[d-1]);let f=e.slice();f[o]=i[d],n.push(f)}return[n,s]}static determineSplit(e,o,i){if(e%o!==0)throw new Error("cannot split tensor to equal sized parts");for(let u=0;u<o;++u)i.push(e/o)}},pr=class _r{static adjustPoolAttributes(e,o,i,u,n,s){if(!e&&i.length!==o.length-2)throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");if(e)for(let d=0;d<o.length-2;d++)d>=i.length?i.push(o[d+2]):i[d]=o[d+2];for(let d=0;d<i.length;d++)if(d<u.length){if(u[d]<0)throw new Error("strides should be greater than or equal to 1")}else u.push(1);for(let d=0;d<i.length;d++)if(d<n.length){if(n[d]<0)throw new Error("dilations should be greater than or equal to 1")}else n.push(1);for(let d=0;d<i.length*2;d++)if(d<s.length){if(s[d]<0)throw new Error("pad should be greater than or equal to 1")}else s.push(0);for(let d=0;d<i.length;d++){if(i[d]<=0)throw new Error("kernel shapes need to be greater than 0");if(s[d]>=i[d]||s[d+i.length]>=i[d])throw new Error("pads should be smaller than kernel")}}static adjustPadsBasedOnAutoPad(e,o,i,u,n,s){if(s){if(n.length!==2*(e.length-2))throw new Error("length of pads should be twice the length of data 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r=i*(u-1)+1;if(f&&f!=="NOTSET")switch(f){case"VALID":return n[s]=0,n[d]=0,Math.floor((e-r)/o+1);case"SAME_LOWER":case"SAME_UPPER":if(i!==1)throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");{let a=((e+o-1)/o-1)*o+u-e;return n[s]=Math.floor(f==="SAME_LOWER"?(a+1)/2:a/2),n[d]=a-n[s],Math.floor((e+a-u)/o+1)}default:throw new Error("Unsupported AutoPad type")}else return Math.floor((e+n[s]+n[d]-r)/o+1)}},dr=-34028234663852886e22,hr=34028234663852886e22});function Vd(t){switch(t){case"bool":case"int8":case"uint8":return 1;case"int16":case"uint16":return 2;case"int32":case"uint32":case"float32":return 4;case"float64":return 8;default:throw new Error(`cannot calculate sizeof() on type ${t}`)}}function Fu(t){switch(t){case te.onnx.TensorProto.DataType.UINT8:case te.onnx.TensorProto.DataType.INT8:case te.onnx.TensorProto.DataType.BOOL:return 1;case te.onnx.TensorProto.DataType.UINT16:case te.onnx.TensorProto.DataType.INT16:return 2;case 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this.data[U.indicesToOffset(e,this.strides)]}set(e,o){this.data[U.indicesToOffset(e,this.strides)]=o}async getData(){return this.cache===void 0&&(this.cache=await this.asyncDataProvider(this.dataId)),this.cache}get strides(){return this._strides||(this._strides=U.computeStrides(this.dims)),this._strides}static fromProto(e){if(!e)throw new Error("cannot construct Value from an empty tensor");let o=Ve.tensorDataTypeFromProto(e.dataType),i=Ve.tensorDimsFromProto(e.dims),u=new Kr(i,o);if(o==="string")e.stringData.forEach((n,s)=>{u.data[s]=sn(n)});else if(e.rawData&&typeof e.rawData.byteLength=="number"&&e.rawData.byteLength>0){let n=u.data,s=new DataView(e.rawData.buffer,e.rawData.byteOffset,e.rawData.byteLength),d=Fu(e.dataType),f=e.rawData.byteLength/d;if(e.rawData.byteLength%d!==0)throw new Error("invalid buffer length");if(n.length!==f)throw new Error("buffer length mismatch");for(let r=0;r<f;r++){let a=$u(s,e.dataType,r*d);n[r]=a}}else{let n;switch(e.dataType){case te.onnx.TensorProto.DataType.FLOAT:n=e.floatData;break;case te.onnx.TensorProto.DataType.INT32:case te.onnx.TensorProto.DataType.INT16:case te.onnx.TensorProto.DataType.UINT16:case te.onnx.TensorProto.DataType.INT8:case te.onnx.TensorProto.DataType.UINT8:case te.onnx.TensorProto.DataType.BOOL:n=e.int32Data;break;case te.onnx.TensorProto.DataType.INT64:n=e.int64Data;break;case te.onnx.TensorProto.DataType.DOUBLE:n=e.doubleData;break;case te.onnx.TensorProto.DataType.UINT32:case te.onnx.TensorProto.DataType.UINT64:n=e.uint64Data;break;default:throw new Error("unspecific error")}if(n==null)throw new Error("failed to populate data from a tensorproto value");let s=u.data;if(s.length!==n.length)throw new Error("array length mismatch");for(let d=0;d<n.length;d++){let f=n[d];zt.isLong(f)?s[d]=bi(f,e.dataType):s[d]=f}}return u}static fromData(e,o,i){return new Kr(o,i,void 0,void 0,e)}static fromOrtTensor(e){if(!e)throw new Error("cannot construct Value from an empty tensor");let o=Ve.tensorDimsFromORTFormat(e),i=Ve.tensorDataTypeFromProto(e.dataType()),u=new Kr(o,i);if(i==="string")for(let n=0;n<e.stringDataLength();n++)u.data[n]=e.stringData(n);else if(e.rawDataArray()&&typeof e.rawDataLength()=="number"&&e.rawDataLength()>0){let n=u.data,s=new DataView(e.rawDataArray().buffer,e.rawDataArray().byteOffset,e.rawDataLength()),d=Fu(e.dataType()),f=e.rawDataLength()/d;if(e.rawDataLength()%d!==0)throw new Error("invalid buffer length");if(n.length!==f)throw new Error("buffer length mismatch");for(let r=0;r<f;r++){let a=$u(s,e.dataType(),r*d);n[r]=a}}return u}}});function q(t){return t===1?Hd:qd}function Bu(t){let e=q(t);return`${e.version}
      precision highp float;
      ${e.attribute} vec3 position;
      ${e.attribute} vec2 textureCoord;

      ${e.varyingVertex} vec2 TexCoords;

      void main()
      {
          gl_Position = vec4(position, 1.0);
          TexCoords = textureCoord;
      }`}function Nu(t){let e=q(t);return`${e.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e.varyingFrag} vec2 TexCoords;
    ${e.outputDeclaration}
    const vec2 halfCR = vec2(0.5, 0.5);

    // Custom vector types to handle higher dimenalities.
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      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    `}function Ru(t,e){let o=q(t);return`
  void main() {
    int indices[${e}];
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    ${o.output} = result;
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        void main() {
          ${s} rc = getOutputCoords();

          if(${a}) {
            ${o.output} = vec4(0);
          } else {
            ${f}

            ${o.output} = vec4(${l});
          }
        }
      `;return{...Gu,hasMain:!0,output:{dims:e.dims,type:e.type,textureType:2},shaderSource:p}},Uu=(t,e)=>({...Gu,get:()=>Yd(t,e)})});function yi(t){if(t.length===0)return[1,1,1];let e=1;for(let o=0;o<t.length-2;++o)e*=t[o];return[e,t.length>1?t[t.length-2]:1,t[t.length-1]]}function Wu(t,e){let o=!1;return t.length===0||e.length===0?o=!0:t.length<2||e.length<2?o=t[t.length-1]===e[e.length-1]:o=t[t.length-1]===e[e.length-1]&&t[t.length-2]===e[e.length-2],o}function eh(t){let e=U.computeStrides(t),o=["b","r","c"],i="index";return`
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
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      return ivec3(b, r, c);
    }
  `}function th(t){let e=U.computeStrides(t);return`
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  }
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        ${a}
        ${r>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
          int flattenedIndex = getFlattenedIndex(outputCoords);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${r}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);

        ${r>0?"}":""}
      `}let d=q(t.session.backend.glContext.version),f=`
      ${eh(u)}
      ${th(n)}
      ${$t()}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.0);

        ivec3 outputCoords;
        int rows = ${n[2]};
        int cols = ${n[1]};

        ${s}
        ${d.output} = result;
      }
    `;return{...o,output:{dims:n,type:e.type,textureType:2},shaderSource:f,hasMain:!0}},Vu=(t,e,o)=>{let i=Zd(o);return{...i,get:()=>Qd(t,e,i,o)}}}),Ti,qu=L(()=>{xe(),ae(),Ti=(t,e)=>{let o=e.shape,i=q(t.session.backend.glContext.version),u=`
    const float FLOAT_MAX = 1.70141184e38;
    const float FLOAT_MIN = 1.17549435e-38;

    bool isNaN(float val) {
      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;
    }

    highp vec4 encodeAsUint8(highp float v) {
      if (isNaN(v)) {
        return vec4(255, 255, 255, 255);
      }

      highp float av = abs(v);

      if(av < FLOAT_MIN) {
        return vec4(0.0, 0.0, 0.0, 0.0);
      } else if(v > FLOAT_MAX) {
        return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
      } else if(v < -FLOAT_MAX) {
        return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
      }

      highp vec4 c = vec4(0,0,0,0);

      highp float e = floor(log2(av));
      highp float m = exp2(fract(log2(av))) - 1.0;

      c[2] = floor(128.0 * m);
      m -= c[2] / 128.0;
      c[1] = floor(32768.0 * m);
      m -= c[1] / 32768.0;
      c[0] = floor(8388608.0 * m);

      highp float ebias = e + 127.0;
      c[3] = floor(ebias / 2.0);
      ebias -= c[3] * 2.0;
      c[2] += floor(ebias) * 128.0;

      c[3] += 128.0 * step(0.0, -v);

      return c / 255.0;
    }

    void main() {
      float value = ${i.texture2D}(X,TexCoords).r;
      ${i.output} = encodeAsUint8(value);
    }`,n={name:"Uint8Encode",inputTypes:[0],inputNames:["X"],output:{dims:o,type:e.tensor.type,textureType:3},shaderSource:u,hasMain:!0};return t.executeProgram(n,[e.tensor])}});function nh(t,e){if(t===1)return"rc";let o="";for(let i=0;i<t;i++)o+=e[i],i<t-1&&(o+=",");return o}var ju,rh,Yu,Xu=L(()=>{xe(),ae(),Ft(),br(),ju={name:"unpack",inputNames:["A"],inputTypes:[2]},rh=(t,e)=>{let o=e.dims.length,i=Nr("rc",o),u=i.slice(-2),n=Ke(o),s=$t(),d=e.dims.length===0?"":nh(o,i),f=o<=1?"rc":`vec2(${u.join(",")})`,r=q(t.session.backend.glContext.version),a=`
    ${s}
    void main() {
      ${n} rc = getOutputCoords();

       // Sample the texture with the coords to get the rgba channel value.
       vec4 packedInput = getA(${d});

       ${r.output} = vec4(getChannel(packedInput, ${f}), 0, 0, 0);
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            ${d[s-1]} = ${d[s-1]} - 1;
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            ${A.output} = result;
          }
        `;return{...e,output:{dims:n,type:o[0].type,textureType:2},shaderSource:F,hasMain:!0}},xl=(t,e,o)=>{let i=Ih(e.length,o.cacheKey);return{...i,get:()=>Sh(t,i,e,o.axis)}},ro=(t,e,o)=>{let i=t.indexOf(e);return t.map((u,n)=>n===i?`${u} - ${o}`:u).join()}}),wl,Ah,Ph,Eh,_l,Dh,Lh,Fh,Ol,$h,Il=L(()=>{Ge(),ae(),vl(),wl=(t,e,o)=>($h(e),t.session.pack&&e[0].dims.length>1?[t.run(xl(t,e,o),e)]:[t.run(Eh(t,e,o),e)]),Ah=(t,e)=>({name:"Concat",inputNames:Array.from({length:t},(o,i)=>`X${i}`),inputTypes:Array(t).fill(0),cacheHint:e}),Ph=(t,e,o,i)=>{let u=o[0].dims.slice();if(i>=u.length||i<-1*u.length)throw new Error("axis specified for concat doesn't match input dimensionality");i<0&&(i=u.length+i);let n=u.slice(0);for(let m=1;m<o.length;m++){let y=o[m].dims.slice();for(let T=0;T<u.length;T++)if(T===i)n[i]+=y[T];else if(u[T]!==y[T])throw new Error("non concat dimensions must match")}let s=n.length,d=new Array(o.length),f=0;for(let m=0;m<d.length;++m)f+=o[m].dims[i],d[m]=f;let r="";o.length<5?r=_l(d):r=Dh(d);let a=Lh(o.length,s),l=Fh(d),p=`
        ${a}
        ${l}
        ${r}
        float process(int indices[${s}]) {
          int textureIndex = getTextureWhereDataResides (indices[${i}]);

          if(textureIndex != 0) {
            indices[${i}] = indices[${i}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));
          }

          return fetchDataFromCorrectTexture(textureIndex, indices);
        }`;return{...e,output:{dims:n,type:o[0].type,textureType:0},shaderSource:p}},Eh=(t,e,o)=>{let i=Ah(e.length,o.cacheKey);return{...i,get:()=>Ph(t,i,e,o.axis)}},_l=t=>`int getTextureWhereDataResides(int index) {
      ${t.map((e,o)=>`if(index<${e}) {return ${o};}
`).join("")}
    }`,Dh=t=>_l(t),Lh=(t,e)=>{let o=[`float fetchDataFromCorrectTexture(int textureIndex, int indices[${e}]) {`];for(let i=0;i<t;++i)i===0?o.push(`	if (textureIndex == ${i}) { return _X${i}(indices); }`):i===t-1?o.push(`	else { return _X${i}(indices); }`):o.push(`	else if (textureIndex == ${i}) { return _X${i}(indices); }`);return o.push("	}"),o.join(`
`)},Fh=t=>{let e=["int getSizeInConcatAxisValueFromIndex(int index) {"];for(let o=0;o<t.length;++o)o===0?e.push(`	if (index == ${o}) { return ${t[o]}; }`):o===t.length-1?e.push(`	else { return ${t[o]}; }`):e.push(`	else if (index == ${o}) { return ${t[o]}; }`);return e.push("	}"),e.join(`
`)},Ol=t=>ee({axis:t.attributes.getInt("axis")}),$h=t=>{if(!t||t.length<1)throw new Error("too few inputs");let e=t[0].type,o=t[0].dims.length;if(e==="string")throw new Error("string tensor is not supported yet");for(let i of t){if(i.type!==e)throw new Error("input tensors should be one type");if(i.dims.length!==o)throw new Error("input tensors should have the same shape")}}});function Ch(){return ht("abs")}function kh(){return ht("acos")}function Bh(){return ht("asin")}function Nh(){return ht("atan")}function Rh(){return ht("ceil")}function Mh(){return ht("cos")}function Gh(t){let e="elu";return{body:`
  const float alpha = float(${t});

  float ${e}_(float a) {
    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
  }
  vec4 ${e}_(vec4 v) {
    return vec4(${e}_(v.x), ${e}_(v.y), ${e}_(v.z), ${e}_(v.w));
  }
  `,name:e,type:0}}function Uh(){return ht("exp")}function zh(){return ht("floor")}function wi(t,e){let o="clip";return{body:`
  const float min = float(${t});
  const float max = float(${e});

  float ${o}_(float a) {
    return clamp(a, min, max);
  }
  vec4 ${o}_(vec4 v) {
    return clamp(v, min, max);
  }
  `,name:o,type:0}}function Vh(){let t="indentity";return{body:`
  float ${t}_(float a) {
    return a;
  }
  vec4 ${t}_(vec4 v) {
    return v;
  }
  `,name:t,type:0}}function Wh(t){let e="leakyRelu";return{body:`
  const float alpha = float(${t});

  float ${e}_(float a) {
    return a < 0.0 ? a * alpha : a;
  }
  vec4 ${e}_(vec4 v) {
    return vec4(${e}_(v.x), ${e}_(v.y), ${e}_(v.z), ${e}_(v.w));
  }
  `,name:e,type:0}}function Hh(){return ht("log")}function qh(){let t="neg";return{body:`
  float ${t}_(float a) {
    return -a;
  }
  vec4 ${t}_(vec4 v) {
    return -v;
  }
  `,name:t,type:0}}function jh(){let t="not";return{body:`
  float ${t}_(float a) {
    return float( ! bool(a) );
  }
  bool ${t}_(bool a) {
    return !a;
  }
  vec4 ${t}_(vec4 v) {
    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
  }
  bvec4 ${t}_(bvec4 v) {
    return bvec4(!v.x, !v.y, !v.z, !v.w);
  }
  `,name:t,type:0}}function Yh(){return ht("sin")}function _i(){let t="relu";return{body:`
  float ${t}_(float a) {
    return max( a, 0.0 );
  }
  vec4 ${t}_(vec4 v) {
    return max( v, 0.0 );
  }
  `,name:t,type:0}}function Oi(){let t="sigmoid";return{body:`
  float ${t}_(float a) {
    return 1.0 / (1.0 + exp(-a));
  }
  vec4 ${t}_(vec4 v) {
    return 1.0 / (1.0 + exp(-v));
  }
  `,name:t,type:0}}function Xh(){return ht("sqrt")}function Kh(){return ht("tan")}function Jh(){let t="tanh";return{body:`
  float ${t}_(float a) {
    a = clamp(a, -10., 10.);
    a = exp(2.*a);
    return (a - 1.) / (a + 1.);
  }
  vec4 ${t}_(vec4 v) {
    v = clamp(v, -10., 10.);
    v = exp(2.*v);
    return (v - 1.) / (v + 1.);
  }
  `,name:t,type:0}}function ht(t){return{body:`
  float ${t}_(float a) {
    return ${t}(a);
  }
  vec4 ${t}_(vec4 v) {
    return ${t}(v);
  }
  `,name:t,type:0}}var Zh,Pe,Sl,Al,Pl,El,Ii,Dl,Ll,Qh,Fl,$l,Cl,kl,Bl,Nl,Si,Rl,Ml,Gl,Ul,zl,Vl,Wl,Hl,ql,jl,Yl,Ai=L(()=>{Ge(),fe(),Vt(),xe(),ae(),Zh=(t,e,o,i)=>{let u=t.session.pack?2:0,n=q(t.session.backend.glContext.version);return{...e,output:{dims:o.dims,type:o.type,textureType:u},shaderSource:`
     ${i.body}
     void main() {
       vec4 v = ${n.texture2D}(A, TexCoords);
       v = ${i.name}_(v);
       ${n.output} = v;
     }
     `,hasMain:!0}},Pe=(t,e,o,i)=>{let u=t.session.pack?2:0,n={name:o.name,inputTypes:[u],inputNames:["A"],cacheHint:i};return{...n,get:()=>Zh(t,n,e,o)}},Sl=(t,e)=>[t.run(Pe(t,e[0],Ch()),e)],Al=(t,e)=>[t.run(Pe(t,e[0],kh()),e)],Pl=(t,e)=>[t.run(Pe(t,e[0],Bh()),e)],El=(t,e)=>[t.run(Pe(t,e[0],Nh()),e)],Ii=(t,e,o)=>[t.run(Pe(t,e[0],wi(o.min,o.max),o.cacheKey),e)],Dl=t=>ee({min:t.attributes.getFloat("min",dr),max:t.attributes.getFloat("max",hr)}),Ll=(t,e)=>{let o=Qh(t,e);return Ii(t,[e[0]],o)},Qh=(t,e)=>{if(e.length>=3&&(!t.session.isInitializer(e[1].dataId)||!t.session.isInitializer(e[2].dataId)))throw new Error("dynamic clip attributes are not allowed");let o=e.length>=3?e[1].numberData[0]:dr,i=e.length>=3?e[2].numberData[0]:hr;return ee({min:o,max:i})},Fl=(t,e)=>[t.run(Pe(t,e[0],Rh()),e)],$l=(t,e)=>[t.run(Pe(t,e[0],Mh()),e)],Cl=(t,e,o)=>[t.run(Pe(t,e[0],Gh(o.alpha),o.cacheKey),e)],kl=t=>ee({alpha:t.attributes.getFloat("alpha",1)}),Bl=(t,e)=>[t.run(Pe(t,e[0],Uh()),e)],Nl=(t,e)=>[t.run(Pe(t,e[0],zh()),e)],Si=(t,e)=>[t.run(Pe(t,e[0],Vh()),e)],Rl=(t,e,o)=>[t.run(Pe(t,e[0],Wh(o.alpha),o.cacheKey),e)],Ml=t=>ee({alpha:t.attributes.getFloat("alpha",.01)}),Gl=(t,e)=>[t.run(Pe(t,e[0],Hh()),e)],Ul=(t,e)=>[t.run(Pe(t,e[0],qh()),e)],zl=(t,e)=>[t.run(Pe(t,e[0],jh()),e)],Vl=(t,e)=>[t.run(Pe(t,e[0],_i()),e)],Wl=(t,e)=>[t.run(Pe(t,e[0],Oi()),e)],Hl=(t,e)=>[t.run(Pe(t,e[0],Yh()),e)],ql=(t,e)=>[t.run(Pe(t,e[0],Xh()),e)],jl=(t,e)=>[t.run(Pe(t,e[0],Kh()),e)],Yl=(t,e)=>[t.run(Pe(t,e[0],Jh()),e)]});function Ct(t){let e;switch(t.activation){case"Relu":e=_i();break;case"Sigmoid":e=Oi();break;case"Clip":e=wi(t.clipMin,t.clipMax);break;default:return{activationFunction:"",applyActivation:""}}let o=e.name,i=e.body,u=`value = ${o}_(value);`;return{activationFunction:i,applyActivation:u}}var Rr,gr=L(()=>{fe(),Ai(),Rr=t=>{let e=t.getString("activation","");if(e==="Clip"){let[o,i]=t.getFloats("activation_params",[dr,hr]);return{activation:e,clipMax:i,clipMin:o,activationCacheKey:`${e}:${o},${i}`}}return{activation:e,activationCacheKey:e}}}),tm,rm,Xl,Kl=L(()=>{ut(),xe(),ae(),no(),gr(),tm=(t,e)=>({name:"GroupedConv",inputNames:t?["X","W","Bias"]:["X","W"],inputTypes:t?[0,0,0]:[0,0],cacheHint:e}),rm=(t,e,o,i)=>{let u=e.length>2?"value += getBias(output_channel);":"",n=e[0].dims.slice(),s=e[1].dims.slice(),d=s[0]/i.group;ce.verbose("GroupedConv",`autpPad:${i.autoPad}, dilations:${i.dilations}, group:${i.group}, kernelShape:${i.kernelShape}, pads:${i.pads}, strides:${i.strides}`);let f=Mr(n,s,i.dilations,i.pads,i.strides),r=q(t.session.backend.glContext.version),{activationFunction:a,applyActivation:l}=Ct(i),p=`
  const ivec2 strides = ivec2(${i.strides[0]}, ${i.strides[1]});
  const ivec2 pads = ivec2(${i.pads[0]}, ${i.pads[1]});
  ${a}
  void main() {
    ivec4 coords = getOutputCoords();
    int batch = coords.x;
    int output_channel = coords.y;
    ivec2 xRCCorner = coords.zw * strides - pads;
    int group_id = output_channel / ${d};

    float value = 0.0;
    for (int wInChannel = 0; wInChannel < ${s[1]}; wInChannel++) {
      int input_channel = group_id * ${s[1]} + wInChannel;
      for (int wHeight = 0; wHeight < ${s[2]}; wHeight++) {
        int xHeight = xRCCorner.x + wHeight * ${i.dilations[0]};

        if (xHeight < 0 || xHeight >= ${n[2]}) {
          continue;
        }

        for (int wWidth = 0; wWidth < ${s[3]}; wWidth++) {
          int xWidth = xRCCorner.y + wWidth * ${i.dilations[1]};
          if (xWidth < 0 || xWidth >= ${n[3]}) {
            continue;
          }

          float xVal = getX(batch, input_channel, xWidth, xHeight);
          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
          value += xVal*wVal;
        }
      }
    }
    ${u}
    ${l}
    ${r.output} = vec4(value, .0, .0, .0);
  }
`;return{...o,output:{dims:f,type:e[0].type,textureType:0},shaderSource:p,hasMain:!0}},Xl=(t,e,o)=>{let i=tm(e.length>2,o.cacheKey);return{...i,get:()=>rm(t,e,i,o)}}}),nm,om,Jl,Zl=L(()=>{xe(),ae(),br(),nm=t=>({name:"Im2Col (packed)",inputNames:["A"],inputTypes:[2],cacheHint:t}),om=(t,e,o,i,u,n)=>{let s=o.dims,d=i.dims,f=2,r=3,a=u.length,l=[d[1]*d[2]*d[3],u[2]*u[3]],p=d[2]*d[3],m=$t(),y=q(t.session.backend.glContext.version),T="";for(let S=0;S<=1;S++)for(let E=0;E<=1;E++)T+=`
            blockIndex = rc.x + ${E};
            pos = rc.y + ${S};

            if(blockIndex < ${l[1]} && pos < ${l[0]}) {
              offsetY = int(blockIndex / (${u[a-1]})) * ${n.strides[0]} -
                ${n.pads[0]};
              d0 = offsetY + ${n.dilations[0]} * (imod(pos, ${p}) / ${d[2]});

              if(d0 < ${s[f]} && d0 >= 0) {
                offsetX = imod(blockIndex, ${u[a-1]}) * ${n.strides[1]} -
                  ${n.pads[1]};
                d1 = offsetX + ${n.dilations[1]} * imod(imod(pos, ${p}), ${d[2]});

                if(d1 < ${s[r]} && d1 >= 0) {

                  ch = int(float(pos)/ ${p}.);
                    innerDims = vec2(d0, d1);
                    result[${S*2+E}] = getChannel(
                      getA(0, ch, int(innerDims.x),
                      int(innerDims.y)), innerDims);
                }
              }
            }

          `;let v=`
      ${m}

      void main() {
        ivec2 rc = getOutputCoords();
          vec4 result = vec4(0.0);
          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
          vec2 innerDims;
          ${T}
          ${y.output} = result;
      }
            `;return{...e,output:{dims:l,type:o.type,textureType:2},shaderSource:v,hasMain:!0}},Jl=(t,e,o,i,u)=>{let n=nm(u.cacheKey);return{...n,get:()=>om(t,n,e,o,i,u)}}});function am(t,e,o){let i=e[0].dims,u=e[1].dims,n=Xe.calcShape(i,u,!0);if(!n)throw new Error("Can't use matmul on the given tensors");let s=Ke(n.length),d=St(),{activationFunction:f,applyActivation:r}=Ct(o),a=e.length>2,l=a?"value += getBiasForMatmul();":"",p=a?`${Ei(s,d,e[2].dims,n,!1)}`:"",m=n.length,y=i.length,T=u.length,v=i[i.length-1],S=`
    ${f}
    ${p}
    float process(int indices[${m}]) {
        int a[${y}];
        int b[${T}];
        bcastMatmulIndices_A(indices, a);
        bcastMatmulIndices_B(indices, b);

        float value;
        for (int k=0; k<${v}; ++k) {
            a[${y-1}] = k;
            b[${T-2}] = k;
            value += _A(a) * _B(b);
        }
        ${l}
        ${r}
        return value;
    }`;return{...t,output:{dims:n,type:e[0].type,textureType:0},shaderSource:S}}function Pi(t,e){let o=im(t.length>2,e.activationCacheKey);return{...o,get:()=>am(o,t,e)}}function Ei(t,e,o,i,u){let n="",s=o.length,d=i.length,f=d-s;d<2&&s>0?n="coords":n=o.map((p,m)=>`coords.${e[m+f]}`).join(", ");let r=Xe.getBroadcastDims(o,i).map(p=>`coords.${e[p+f]} = 0;`).join(`
`),a=U.size(o)===1,l="vec4(outputValue.xx, outputValue.yy)";return a&&(l="vec4(outputValue.x)"),u?`
vec4 getBiasForMatmul() {
  ${t} coords = getOutputCoords();
  ${r}
  vec4 outputValue = getBias(${n});
  return ${l};
}`:`
float getBiasForMatmul() {
  ${t} coords = getOutputCoords();
  ${r}
  return getBias(coords.x);
}`}var Ql,ef,im,sm,oo=L(()=>{fe(),ae(),Ft(),gr(),Di(),Ql=(t,e,o)=>(sm(e),t.session.pack?[t.run(io(t,e,o),e)]:[t.run(Pi(e,o),e)]),ef=t=>Rr(t.attributes),im=(t,e)=>({name:"MatMul",inputNames:t?["A","B","Bias"]:["A","B"],inputTypes:t?[0,0,0]:[0,0],cacheHint:e}),sm=t=>{if(!t||t.length!==2)throw new Error("MatMul requires 2 inputs.");if(t[0].dims[t[0].dims.length-1]!==t[1].dims[t[1].dims.length-2])throw new Error("shared dimension does not match.");if(t[0].type!=="float32"&&t[0].type!=="float64"||t[1].type!=="float32"&&t[1].type!=="float64")throw new Error("inputs should be float type");if(t[0].type!==t[1].type)throw new Error("inputs types should match")}});function fm(t,e,o,i){let u=[],n=[],s=o[0].dims,d=o[1].dims,f=s.length,r=d.length,a=i.length,l=a-f,p=a-r;u=s.map((E,A)=>`coords.${e[A+l]}`),u[f-1]="i*2",u.join(", "),n=d.map((E,A)=>`coords.${e[A+p]}`),n[r-2]="i*2",n.join(", ");let m=Xe.getBroadcastDims(s,i),y=Xe.getBroadcastDims(d,i),T=m.map(E=>`coords.${e[E+l]} = 0;`).join(`
`),v=y.map(E=>`coords.${e[E+p]} = 0;`).join(`
`),S=`int lastDim = coords.${e[a-1]};
  coords.${e[a-1]} = coords.${e[a-2]};
  coords.${e[a-2]} = lastDim;`;return`
vec4 getAAtOutCoordsMatmul(int i) {
  ${t} coords = getOutputCoords();
  ${S}
  ${T}
  vec4 outputValue = getA(${u});
  return outputValue;
}

vec4 getBAtOutCoordsMatmul(int i) {
  ${t} coords = getOutputCoords();
  ${S}
  ${v}
  vec4 outputValue = getB(${n});
  return outputValue;
}`}function cm(t,e){let o="";for(let i=0;i<e-2;i++)o+=`rc.${t[i]}, `;return o+=`rc.${t[e-2]}, i*2`,o}function pm(t,e){let o="";for(let i=0;i<e-2;i++)o+=`rc.${t[i]}, `;return o+=`i*2, rc.${t[e-1]}`,o}var um,lm,io,Di=L(()=>{fe(),xe(),ae(),Ft(),gr(),oo(),um=(t,e)=>({name:"MatMul (packed)",inputNames:t?["A","B","Bias"]:["A","B"],inputTypes:t?[2,2,2]:[2,2],cacheHint:e}),lm=(t,e,o,i)=>{let u=o.length>2,n=u?"value += getBiasForMatmul();":"",s=o[0].dims,d=o[1].dims,f=Xe.calcShape(s,d,!0),r=!U.areEqual(o[0].dims,o[1].dims);if(!f)throw new Error("Can't use matmul on the given tensors");let a=s[s.length-1],l=Math.ceil(a/2),p=s.length,m=d.length,y=q(t.session.backend.glContext.version),T=Ke(f.length),v=f.length,S=St(),{activationFunction:E,applyActivation:A}=Ct(i),F=u?`${Ei(T,S,o[2].dims,f,!0)}`:"",B=r?`${fm(T,S,o,f)}`:"",J=r?"getAAtOutCoordsMatmul(i)":`getA(${cm(S,p)})`,Q=r?"getBAtOutCoordsMatmul(i)":`getB(${pm(S,m)})`,re=r?"":`${T} rc =
          getOutputCoords(); int lastDim = rc.${S[v-1]}; rc.${S[v-1]} =
          rc.${S[v-2]}; rc.${S[v-2]} = lastDim;
      `,V=`
            ${B}
            ${F}
            ${E}
            void main() {
              ${re}

              vec4 value = vec4(0);
              for (int i = 0; i < ${l}; i++) {
                vec4 a = ${J};
                vec4 b = ${Q};

                value += (a.rrbb * b.rgrg);
                value += (a.ggaa * b.baba);
              }
              ${n}
              ${A}
              ${y.output} = value;
            }`;return{...e,output:{dims:f,type:o[0].type,textureType:2},shaderSource:V,hasMain:!0}},io=(t,e,o)=>{let i=um(e.length>2,o.activationCacheKey);return{...i,get:()=>lm(t,i,e,o)}}}),tf,rf=L(()=>{no(),Zl(),Di(),tf=(t,e,o)=>{let i=e[0].dims,u=e[1].dims,n=Mr(i,u,o.dilations,o.pads,o.strides),s=t.run(Jl(t,e[0],e[1],n,o),[e[0]]),d=t.reshapePacked(e[1],[u[0],u[1]*u[2]*u[3]]),f=e.length===3?[d,s,e[2]]:[d,s],r=t.run(io(t,f,o),f);return t.reshapePacked(r,n)}}),dm,hm,nf,Li,Fi=L(()=>{ae(),dm=t=>({name:"Im2Col",inputNames:["X"],inputTypes:[0],cacheHint:t}),hm=(t,e,o,i,u,n)=>{let s=o.dims,d=i.dims,f=u.length,r=Li(s,d,u,4),a=`
        const int XC = ${s[1]};
        const int XH = ${s[2]};
        const int XW = ${s[3]};
        const int KH = ${n.kernelShape[0]};
        const int KW = ${n.kernelShape[1]};
        const int dilationH = ${n.dilations[0]};
        const int dilationW = ${n.dilations[1]};
        const int strideH = ${n.strides[0]};
        const int strideW = ${n.strides[1]};
        const int padH = ${n.pads[0]};
        const int padW = ${n.pads[1]};
        const int KHKW = KH*KW;
        const int XCKHKW = XC * KHKW;
        const int outputChannels = 4;
        vec4 process(int indices[${f}]) {
          int b  = indices[0]; // batch size
          int oh = indices[1] * strideH - padH; //output height
          int ow = indices[2] * strideW - padW; //output width
          int p = indices[3] * outputChannels; //patch
          vec4 value = vec4(0.0);
          for(int i=0; i < outputChannels; ++i) {
            if(p < XCKHKW) {
              int patchC = p / KHKW;
              int patchH = (p - patchC*KHKW) / KW;
              int patchW = (p - patchC*KHKW) - patchH * KW;
              int xh2 = oh + patchH * dilationH;
              int xw2 = ow + patchW * dilationW;
              int x[${s.length}];
              x[0] = b;
              x[1] = patchC;
              x[2] = xh2;
              x[3] = xw2;
              if(xh2 >= 0 &&
                  xh2 < XH &&
                  xw2 >= 0 &&
                  xw2 < XW) {
                value[i] = _X(x);
              }
            }
            ++p;
          }
          return value;
        }
        `;return{...e,output:{dims:r,type:o.type,textureType:4},shaderSource:a}},nf=(t,e,o,i,u)=>{let n=dm(u.cacheKey);return{...n,get:()=>hm(t,n,e,o,i,u)}},Li=(t,e,o,i=4)=>[o[0],o[2],o[3],Math.ceil(t[1]*e[2]*e[3]/i)]}),mm,bm,of,af=L(()=>{fe(),xe(),ae(),gr(),Fi(),mm=(t,e)=>({name:"ConvDotProduct",inputNames:t?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:t?[0,4,0]:[0,4],cacheKey:e.activationCacheKey}),bm=(t,e,o,i,u)=>{let n=o[0].dims,s=o[1].dims,d=[s[0],Math.ceil(n[1]*s[2]*s[3]/4)],f=Li(n,s,i),[r,a]=t.calculateTextureWidthAndHeight(d,4),l=U.computeStrides(f),[p,m]=t.calculateTextureWidthAndHeight(f,4),y=i.length,T=o.length<3?"0.0":"_B(b)",v=Math.ceil(n[1]*s[2]*s[3]/4),{activationFunction:S,applyActivation:E}=Ct(u),A=q(t.session.backend.glContext.version),F=`
${S}
float process(int indices[${y}]) {
  int b[1];
  b[0] = indices[1];
  int im2col[4];
  im2col[0] = indices[0];
  im2col[1] = indices[2];
  im2col[2] = indices[3];
  int im2colOffset = im2col[0] * ${l[0]} + im2col[1] * ${l[1]} + im2col[2] * ${l[2]};
  int kernelOffset = indices[1] * ${d[1]};
  float value = ${T};
  for (int i = 0; i < ${v}; ++i) {
    vec2 im2colCoords = offsetToCoords(im2colOffset, ${p}, ${m});
    vec2 kernelCoords = offsetToCoords(kernelOffset, ${r}, ${a});
    value += dot(${A.texture2D}(Im2Col, im2colCoords), ${A.texture2D}(K, kernelCoords));
    ++im2colOffset;
    ++kernelOffset;
  }
  ${E}
  return value;
}`;return{...e,output:{dims:i,type:o[0].type,textureType:0},shaderSource:F}},of=(t,e,o,i)=>{let u=mm(e.length>2,i);return{...u,get:()=>bm(t,u,e,o,i)}}}),Mr,$i,gm,ym,Tm,xm,Ci,vm,no=L(()=>{Ge(),fe(),Kl(),rf(),af(),gr(),Fi(),oo(),Mr=(t,e,o,i,u)=>{let n=t[0],s=t.slice(2),d=s.length,f=e[0],r=e.slice(2).map((l,p)=>l+(l-1)*(o[p]-1)),a=s.map((l,p)=>l+i[p]+i[p+d]).map((l,p)=>Math.floor((l-r[p]+u[p])/u[p]));return[n,f].concat(...a)},$i=(t,e,o)=>(vm(e,o),gm(t,e,o)),gm=(t,e,o)=>{let i=xm(o,e),u=t.session.pack,n=i.kernelShape[0]===1&&i.kernelShape[1]===1;return i.group>1?[t.run(Xl(t,e,i),e)]:n&&u?[ym(t,e,i)]:u&&e[0].dims.length===4&&e[0].dims[0]===1&&!n?[tf(t,e,i)]:[Tm(t,e,i)]},ym=(t,e,o)=>{let i=e[0].dims,u=e[1].dims,n=Mr(i,u,o.dilations,o.pads,o.strides),s=t.reshapeUnpacked(e[0],[i[1],i[2]*i[3]]),d=t.reshapeUnpacked(e[1],[u[0],u[1]]),f=e.length>2?[d,s,e[2]]:[d,s],r=t.run(Pi(f,o),f);return t.reshapeUnpacked(r,n)},Tm=(t,e,o)=>{let i=e[0].dims,u=e[1].dims,n=Mr(i,u,o.dilations,o.pads,o.strides),s=t.run(nf(t,e[0],e[1],n,o),[e[0]]),d=e.length===3?[s,e[1],e[2]]:[s,e[1]];return t.run(of(t,e,n,o),d)},xm=(t,e)=>{let o=t.kernelShape.slice();if(t.kernelShape.length===0)for(let n=2;n<e[1].dims.length;++n)o.push(e[1].dims[n]);let i=t.pads.slice();pr.adjustPadsBasedOnAutoPad(e[0].dims,t.strides,t.dilations,o,i,t.autoPad);let u=Object.assign({},t);return Object.assign(u,{kernelShape:o,pads:i,cacheKey:t.cacheKey}),u},Ci=t=>{let e=t.attributes,o=Rr(e),i=e.getString("auto_pad","NOTSET"),u=e.getInts("dilations",[1,1]),n=e.getInt("group",1),s=e.getInts("kernel_shape",[]),d=e.getInts("pads",[0,0,0,0]),f=e.getInts("strides",[1,1]);return ee({autoPad:i,dilations:u,group:n,kernelShape:s,pads:d,strides:f,...o})},vm=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4||t[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");let o=t[0].dims[1],i=t[1].dims[1]*e.group;if(o!==i)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");if(t.length===3&&(t[2].dims.length!==1||t[1].dims[0]!==t[2].dims[0]))throw new Error("invalid bias");let u=t[0].dims.length-2;if(e.dilations.length!==u)throw new Error(`dilations should be ${u}D`);if(e.strides.length!==u)throw new Error(`strides should be ${u}D`);if(e.pads.length!==u*2)throw new Error(`pads should be ${u*2}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(t[0].type!=="float32"||t[1].type!=="float32")throw new Error("Conv input(X,W) should be float tensor");if(t.length===3&&t[2].type!=="float32")throw new Error("Conv input(bias) should be float tensor")}}),wm,_m,Om,sf,Im,Sm,Am,Pm,Em,Dm,uf,Lm,lf=L(()=>{Ge(),xe(),ae(),gr(),wm=(t,e,o,i,u,n)=>(t-1)*e+o+(i-1)*u+1-n,_m=(t,e,o,i,u)=>{let n=Math.floor(t/2);e==="SAME_UPPER"?(o[i]=n,o[u]=t-n):e==="SAME_LOWER"&&(o[i]=t-n,o[u]=n)},Om=(t,e,o,i,u,n,s,d)=>{let f=t.length-2,r=d.length===0;for(let a=0;a<f;++a){let l=r?t[a+2]*n[a]:d[a],p=wm(t[a+2],n[a],u[a],e[a],o[a],l);_m(p,i,u,a,a+f),r&&d.push(n[a]*(t[a+2]-1)+s[a]+(e[a]-1)*o[a]+1-u[a]-u[a+f])}},sf=(t,e,o)=>(Lm(e,o),Im(t,e,o)),Im=(t,e,o)=>{let i=Dm(o,e);return[Em(t,e,i)]},Sm=(t,e)=>({name:"ConvTranspose",inputNames:t?["X","W","B"]:["X","W"],inputTypes:t?[0,0,0]:[0,0],cacheHint:e}),Am=(t,e,o,i)=>{let u=e.length>2?"getB(output_channel)":"0.0",n=e[0].dims,s=e[1].dims,d=s[1],f=s[0]/i.group,r=[e[0].dims[0],e[1].dims[1]*i.group,...i.outputShape],a=q(t.session.backend.glContext.version),{activationFunction:l,applyActivation:p}=Ct(i),m=`
  const ivec2 strides = ivec2(${i.strides[0]}, ${i.strides[1]});
  const ivec2 pads = ivec2(${i.pads[0]}, ${i.pads[1]});
  ${l}
  void main() {
    ivec4 coords = getOutputCoords();
    int batch = coords.x;
    int output_channel = coords.y;

    ivec2 loc = coords.zw + pads;

    int group_id = output_channel / ${d};
    int wOutChannel = output_channel - group_id * ${d};

    float value = ${u};
    for (int inChannelOffset = 0; inChannelOffset < ${f}; inChannelOffset++) {
      int input_channel = group_id * ${f} + inChannelOffset;
      for (int wWOff = 0; wWOff < ${s[2]}; wWOff++) {
        for (int wHOff = 0; wHOff < ${s[3]}; wHOff++) {
          ivec2 wOff = ivec2(wWOff * ${i.dilations[0]}, wHOff * ${i.dilations[1]});
          ivec2 wLoc = loc - wOff;
          ivec2 wLocIn = wLoc / strides;
          if (
            wLocIn * strides == wLoc &&
            wLocIn.x >= 0 && wLocIn.x < ${n[2]} &&
            wLocIn.y >= 0 && wLocIn.y < ${n[3]}
          ) {
            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
            value += xVal * wVal;
          }
        }
      }
    }
    ${p}
    ${a.output} = vec4(value, .0, .0, .0);
  }
`;return{...o,output:{dims:r,type:e[0].type,textureType:0},shaderSource:m,hasMain:!0}},Pm=(t,e,o)=>{let i=Sm(e.length>2,o.cacheKey);return{...i,get:()=>Am(t,e,i,o)}},Em=(t,e,o)=>t.run(Pm(t,e,o),e),Dm=(t,e)=>{let o=t.kernelShape.slice();if(t.kernelShape.length===0)for(let d=2;d<e[1].dims.length;++d)o.push(e[1].dims[d]);let i=t.pads.slice(),u=t.outputShape.slice(),n=e[0].dims;Om(n,o,t.dilations,t.autoPad,i,t.strides,t.outputPadding,u);let s=Object.assign({},t);return Object.assign(s,{kernelShape:o,pads:i,outputShape:u,cacheKey:t.cacheKey}),s},uf=t=>{let e=t.attributes,o=Rr(e),i=e.getString("auto_pad","NOTSET"),u=e.getInts("dilations",[1,1]),n=e.getInt("group",1),s=e.getInts("kernel_shape",[]),d=e.getInts("output_padding",[0,0]),f=e.getInts("output_shape",[]),r=e.getInts("pads",[0,0,0,0]),a=e.getInts("strides",[1,1]);return ee({autoPad:i,dilations:u,group:n,kernelShape:s,outputPadding:d,outputShape:f,pads:r,strides:a,...o})},Lm=(t,e)=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Conv requires 2 or 3 inputs");if(t[0].dims.length!==4||t[1].dims.length!==4)throw new Error("currently only support 2-dimensional conv");let o=t[0].dims[1],i=t[1].dims[0];if(o!==i)throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");let u=t[1].dims[1]*e.group;if(t.length===3&&(t[2].dims.length!==1||t[2].dims[0]!==u))throw new Error("invalid bias");let n=t[0].dims.length-2;if(e.dilations.length!==n)throw new Error(`dilations should be ${n}D`);if(e.strides.length!==n)throw new Error(`strides should be ${n}D`);if(e.pads.length!==n*2)throw new Error(`pads should be ${n*2}D`);if(e.outputPadding.length!==n)throw new Error(`output_padding should be ${n}D`);if(e.kernelShape.length!==0&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(e.outputShape.length!==0&&e.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape");if(t[0].type!=="float32"||t[1].type!=="float32")throw new Error("ConvTranspose input(X,W) should be float tensor");if(t.length===3&&t[2].type!=="float32")throw new Error("ConvTranspose input(bias) should be float tensor")}}),ff,yr,cf,Fm,pf,$m,Cm,km,ao=L(()=>{Ge(),fe(),ae(),ff={name:"Transpose",inputNames:["A"],inputTypes:[0]},yr=(t,e,o)=>(km(e),[t.run({...ff,cacheHint:o.cacheKey,get:()=>Fm(t,e[0],o.perm)},e)]),cf=t=>ee({perm:t.attributes.getInts("perm",[])}),Fm=(t,e,o)=>{let i=e.dims;o=pf(i,o);let u=$m(i,o),n=i.length,s=`
      ${Cm("perm",o,n)}
      float process(int indices[${n}]) {
        int a[${n}];
        perm(a, indices);
        return _A(a);
      }`;return{...ff,output:{dims:u,type:e.type,textureType:0},shaderSource:s}},pf=(t,e)=>(e&&e.length!==t.length&&(e=[...t.keys()].reverse()),e),$m=(t,e)=>(e=pf(t,e),U.sortBasedOnPerm(t,e)),Cm=(t,e,o)=>{let i=[];i.push(`void ${t}(out int a[${o}], int src[${o}]) {`);for(let u=0;u<o;++u)i.push(`	a[${e[u]}]=src[${u}];`);return i.push("	}"),i.join(`
`)},km=t=>{if(!t||t.length!==1)throw new Error("Transpose requires 1 input.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("input should be float tensor")}}),df,hf,Bm,mf=L(()=>{ao(),df=(t,e,o)=>{Bm(e);let i=o.blocksize,u=i*i,n=o.mode==="DCR"?[0,3,4,1,5,2]:[0,1,4,2,5,3],s=o.mode==="DCR"?[e[0].dims[0],i,i,e[0].dims[1]/u,e[0].dims[2],e[0].dims[3]]:[e[0].dims[0],e[0].dims[1]/u,i,i,e[0].dims[2],e[0].dims[3]],d=t.reshapeUnpacked(e[0],s),f={perm:n,cacheKey:`${n}`},[r]=yr(t,[d],f),a=[e[0].dims[0],e[0].dims[1]/u,e[0].dims[2]*i,e[0].dims[3]*i];return[t.reshapeUnpacked(r,a)]},hf=t=>{let e=t.attributes.getInt("blocksize");if(e<1)throw new Error(`blocksize must be >= 1, but got : ${e} for DepthToSpace`);let o=t.attributes.getString("mode","DCR");if(o!=="DCR"&&o!=="CRD")throw new Error(`unrecognized mode: ${o} for DepthToSpace`);return{mode:o,blocksize:e}},Bm=t=>{if(t.length!==1)throw new Error(`DepthToSpace expect 1 inputs, but got ${t.length}`);if(t[0].type==="string"||t[0].dims.length!==4)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}}),bf,gf,Nm,yf=L(()=>{fe(),bf=(t,e,o)=>{Nm(e,o);let i=U.flattenShape(e[0].dims,o);return[t.reshapeUnpacked(e[0],i)]},gf=t=>t.attributes.getInt("axis",1),Nm=(t,e)=>{if(!t||t.length!==1)throw new Error("Flatten requires 1 input.");let o=t[0].dims.length;if(o===0)throw new Error("scalar tensor is not supported.");if(e<-o||e>o)throw new Error("Invalid axis");if(t[0].type==="string")throw new Error("string tensor is not supported.")}}),Jt,pn=L(()=>{Jt=["float32","float64","int32","int16","int8","uint16","uint32","uint8"]}),Tf,xf,Rm,Mm,Gm,Um,vf=L(()=>{Ge(),pn(),fe(),ae(),Tf=(t,e,o)=>(Um(e,o.axis),[t.run(Gm(t,e,o),e)]),xf=t=>ee({axis:t.attributes.getInt("axis",0)}),Rm={name:"Gather",inputNames:["A","B"],inputTypes:[0,0]},Mm=(t,e,o,i)=>{let u=o[0].dims.slice(),n=o[1].dims.slice(),s=new Array(u.length+n.length-1);i=U.normalizeAxis(i,u.length);let d=[];for(let p=0;p<s.length;p++)p<i?(s[p]=u[p],d.push(`inputIdx[${p}] = outputIdx[${p}];`)):p<i+n.length?(s[p]=n[p-i],d.push(`indexDataIdx[${p-i}] = outputIdx[${p}];`)):(s[p]=u[p-n.length+1],d.push(`inputIdx[${p-n.length+1}] = outputIdx[${p}];`));let f=s.length||1,r=u.length,a=n.length||1,l=`
      float process(int outputIdx[${f}]) {
        int inputIdx[${r}];
        int indexDataIdx[${a}];
        indexDataIdx[0] = 0;
        ${d.join(`
        `)}
        int idx = int(_B(indexDataIdx));
        inputIdx[${i}] = idx < 0 ? idx + ${u[i]} : idx;
        return _A(inputIdx);
      }`;return{...e,output:{dims:s,type:o[0].type,textureType:0},shaderSource:l}},Gm=(t,e,o)=>{let i={...Rm,cacheHint:o.cacheKey};return{...i,get:()=>Mm(t,i,e,o.axis)}},Um=(t,e)=>{if(!t||t.length!==2)throw new Error("Gather requires 2 inputs.");let o=t[0].dims.length;if(o<1)throw new Error("Invalid input shape.");if(e<-o||e>o-1)throw new Error("Invalid axis.");if(Jt.indexOf(t[0].type)===-1)throw new Error("Invaid input type.");if(t[1].type!=="int32"&&t[1].type!=="int16")throw new Error("Invaid input type.")}}),ki,wf,_f,Of,zm,Vm,Wm,If=L(()=>{Ge(),fe(),ae(),ki=(t,e,o)=>(Wm(e,o),[t.run(zm(e,o),e)]),wf=(t,e)=>{let o=t.attributes.getInt("transA",0)!==0,i=t.attributes.getInt("transB",0)!==0,u=t.attributes.getFloat("alpha",1),n=t.attributes.getFloat("beta",1);return ee({transA:o,transB:i,alpha:u,beta:n,isOptionalC:e})},_f=t=>wf(t,!1),Of=t=>wf(t,!0),zm=(t,e)=>{let o={name:"Gemm",inputNames:t.length===3?["A","B","C"]:["A","B"],inputTypes:t.length===3?[0,0,0]:[0,0],key:e.cacheKey};return{...o,get:()=>Vm(o,t,e)}},Vm=(t,e,o)=>{let i=e[0].dims.slice(),u=e[1].dims.slice(),[n,s]=Xn.getShapeOfGemmResult(i,o.transA,u,o.transB,e.length===3?e[2].dims:void 0),d=[n,s];if(!d)throw new Error("Can't use gemm on the given tensors");let f=i[i.length-1],r="";o.transA&&(f=i[0]),o.transA&&o.transB?r="value += _A_T(a) * _B_T(b);":o.transA&&!o.transB?r="value += _A_T(a) * _B(b);":!o.transA&&o.transB?r="value += _A(a) * _B_T(b);":!o.transA&&!o.transB&&(r="value += _A(a) * _B(b);");let a=d.length,l=e.length===3?`int c[${e[2].dims.length}];`:"",p=e.length===3?"bcastIndices_C(indices, c);":"",m=e.length===3?"value += beta * _C(c);":"",y=`
      float process(int indices[${a}]) {
          int a[${a}];
          int b[${a}];
          ${l}

          copyVec(indices, a);
          copyVec(indices, b);
          ${p}

          float value = 0.0;
          for (int k=0; k<${f}; ++k) {
              a[${a-1}] = k;
              b[${a-2}] = k;
              ${r}
          }

          value = value * alpha;
          ${m}
          return value;
      }`;return{...t,output:{dims:d,type:e[0].type,textureType:0},variables:[{name:"alpha",type:"float",data:o.alpha},{name:"beta",type:"float",data:o.beta}],shaderSource:y}},Wm=(t,e)=>{if(!t)throw new Error("Input is missing");if(e.isOptionalC&&(t.length<2||t.length>3))throw new Error("Invaid input shape.");if(!e.isOptionalC&&t.length!==3)throw new Error("Gemm requires 3 inputs");if(t.length===3&&t[2].dims.length!==1&&t[2].dims.length!==2)throw new Error("Invalid input shape of C");if(t[0].type!=="float32"&&t[0].type!=="float64"||t[1].type!=="float32"&&t[1].type!=="float64"||t.length===3&&t[2].type!=="float32"&&t[2].type!=="float64")throw new Error("Invalid input type.");if(t[0].type!==t[1].type||t.length===3&&t[0].type!==t[2].type)throw new Error("Input types are mismatched")}}),Sf,Af,Hm,qm,jm,Ym,Xm,Pf=L(()=>{Ge(),ae(),Sf=(t,e,o)=>(Xm(e),[t.run(jm(t,e,o),e)]),Af=t=>{let e=t.attributes.getFloat("scale"),o=t.attributes.getFloats("bias");return ee({scale:e,bias:o})},Hm={name:"ImageScaler",inputNames:["X"],inputTypes:[0]},qm=(t,e,o,i)=>{let u=o[0].dims.slice(),n=u.length,s=`
      ${Ym(i.bias.length)}
      float process(int indices[${n}]) {
        return _X(indices) * scale + getBias(bias, indices[1]);
      }`;return{...e,output:{dims:u,type:o[0].type,textureType:0},variables:[{name:"bias",type:"float",arrayLength:i.bias.length,data:i.bias},{name:"scale",type:"float",data:i.scale}],shaderSource:s}},jm=(t,e,o)=>{let i={...Hm,cacheHint:o.cacheKey};return{...i,get:()=>qm(t,i,e,o)}},Ym=t=>{let e=[`float getBias(float bias[${t}], int channel) {`];for(let o=0;o<t;++o)o===0?e.push(`	if (channel == ${o}) { return bias[${o}]; }`):o===t-1?e.push(`	else { return bias[${o}]; }`):e.push(`	else if (channel == ${o}) { return bias[${o}]; }`);return e.push("	}"),e.join(`
`)},Xm=t=>{if(!t||t.length!==1)throw new Error("ImageScaler requires 1 input.");if(t[0].dims.length!==4)throw new Error("Invalid input shape.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type.")}}),Df,Lf,Ef,Km,Jm,Zm,Qm,eb,tb,Ff=L(()=>{xe(),ae(),Df=(t,e,o)=>{tb(e);let i=t.run(Jm(e[0]),e);return[t.run(eb(t,e[0],o,i.dims),[e[0],i,e[1],e[2]])]},Lf=t=>t.attributes.getFloat("epsilon",1e-5),Ef={name:"InstanceNormalization_MeanAndVariance",inputNames:["X"],inputTypes:[0]},Km=(t,e)=>{let o=e.dims.slice(),i=o[1],u=o[2]*o[3],n=[o[0],i],s=`
      vec4 process(int[2] indices) {
        vec4 v = vec4(0.0);
        int a[4];
        a[0] = indices[0];
        a[1] = indices[1];
        float temp = 0.0;
        for(int a2=0; a2<${o[2]}; a2++) {
          a[2] = a2;
          for(int a3=0; a3<${o[3]}; a3++) {
            a[3] = a3;
            float x = _X(a);
            temp += x;
          }
        }
        float mean = temp / float(${u});
        temp = 0.0;
        for(int a2=0; a2<${o[2]}; a2++) {
          a[2] = a2;
          for(int a3=0; a3<${o[3]}; a3++) {
            a[3] = a3;
            float x = _X(a);
            temp += (x - mean) * (x - mean);
          }
        }
        v.r = mean;
        v.g = temp / float(${u});

        return v;
      }`;return{...t,output:{dims:n,type:e.type,textureType:4},shaderSource:s}},Jm=t=>({...Ef,get:()=>Km(Ef,t)}),Zm={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[0,4,0,0]},Qm=(t,e,o,i,u)=>{let n=q(t.session.backend.glContext.version),[s,d]=t.calculateTextureWidthAndHeight(u,4),[f,r]=[s/4,d],a=`
      vec4 get_MeanAndVariance(int[2] mv) {
        int offset = indicesToOffset_MeanAndVariance(mv);
        vec2 coords = offsetToCoords(offset, ${f}, ${r});
        return ${n.texture2D}(MeanAndVariance, coords);
      }

      float process(int[4] indices) {
        int mv[2];
        mv[0] = indices[0];
        mv[1] = indices[1];
        vec4 mean_and_variance = get_MeanAndVariance(mv);
        float mean = mean_and_variance.r;
        float variance = mean_and_variance.g;

        int sb[1];
        sb[0] = indices[1];
        float scale = _Scale(sb);
        float b = _B(sb);

        return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;
      }`;return{...e,output:{dims:o.dims,type:o.type,textureType:0},variables:[{name:"epsilon",type:"float",data:i}],shaderSource:a}},eb=(t,e,o,i)=>{let u={...Zm,cacheHint:`${o}`};return{...u,get:()=>Qm(t,u,e,o,i)}},tb=t=>{if(!t||t.length!==3)throw new Error("InstanceNormalization requires 3 inputs.");let e=t[0],o=t[1],i=t[2];if(e.dims.length<3||o.dims.length!==1||i.dims.length!==1)throw new Error("Invalid input shape.");if(o.dims[0]!==e.dims[1]||i.dims[0]!==e.dims[1])throw new Error("Input shapes are mismatched.");if(e.type!=="float32"&&e.type!=="float64"||o.type!=="float32"&&o.type!=="float64"||i.type!=="float32"&&i.type!=="float64")throw new Error("Invalid input type.");if(t[0].dims.length!==4)throw new Error("Only support 4-D input shape.")}});function rb(t,e){let o=t[0].dims[1],i=t[0].dims.length,u=-Math.floor((e.size-1)/2),n=Math.ceil((e.size-1)/2),s=`float(${e.alpha}) / float(${e.size})`,d=`float(${e.bias})`,f=`float(${e.beta})`,r=`
    float process(int indices[${i}]) {
        int c = indices[1];
        float x = _X(indices);
        float square_sum = 0.0;

        for (int i = ${u}; i <= ${n}; i++) {
          int idx = c + i;
          if (c >= 0 && c < ${o}) {
            indices[1] = idx;
            float j = _X(indices);
            square_sum += j * j;
          }
        }
        return x / pow(${d} + ${s} * square_sum, ${f});
    }`;return{...kf,cacheHint:e.cacheKey,output:{dims:t[0].dims,type:t[0].type,textureType:0},shaderSource:r}}function nb(t,e){return{...kf,cacheHint:e.cacheKey,get:()=>rb(t,e)}}var $f,Cf,kf,ob,Bf=L(()=>{Ge(),ae(),$f=(t,e,o)=>(ob(e),[t.run(nb(e,o),e)]),Cf=t=>{let e=t.attributes.getFloat("alpha",1e-4),o=t.attributes.getFloat("beta",.75),i=t.attributes.getFloat("bias",1),u=t.attributes.getInt("size");return ee({alpha:e,beta:o,bias:i,size:u})},kf={name:"LRN",inputNames:["X"],inputTypes:[0]},ob=t=>{if(!t||t.length!==1)throw new Error("LRN requires 1 input.");if(t[0].dims.length!==4)throw new Error('currently only support LRN for input with "NCHW" format');if(t[0].type!=="float32")throw new Error("input should be float type")}}),ib,Bi,Nf,Rf,Mf,ab,sb,ub,lb,fb,cb,pb,db,Gf=L(()=>{Ge(),fe(),xe(),ae(),ib={name:"Pad",inputNames:["A"],inputTypes:[0]},Bi=(t,e,o)=>(ub(e),[t.run({...ib,cacheHint:o.cacheKey,get:()=>sb(t,e[0],o)},e)]),Nf=t=>{let e=t.attributes.getString("mode","constant"),o=t.attributes.getFloat("value",0),i=t.attributes.getInts("pads");return ee({mode:e,value:o,pads:i})},Rf=(t,e,o)=>{lb(e);let i=ab(t,e,o);return Bi(t,[e[0]],i)},Mf=t=>t.attributes.getString("mode","constant"),ab=(t,e,o)=>{if(!t.session.isInitializer(e[1].dataId)||e.length>=3&&!t.session.isInitializer(e[2].dataId))throw new Error("dynamic pad attributes are not allowed");let i=Array.from(e[1].integerData),u=e.length>=3?e[2].floatData[0]:0;return ee({mode:o,pads:i,value:u})},sb=(t,e,o)=>{let i=U.padShape(e.dims.slice(),o.pads),u=i.length,n=`
      ${fb(t,e,o)}
      float process(int[${u}] indices) {
          return padA(indices);
      }`;return{name:"Pad",inputNames:["A"],inputTypes:[0],output:{dims:i,type:e.type,textureType:0},shaderSource:n}},ub=t=>{if(!t||t.length!==1)throw new Error("Pad requires 1 input");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type.")},lb=t=>{if(!t||t.length!==2&&t.length!==3)throw new Error("Pad requires 2 or 3 inputs");if(t[1].type!=="int32")throw new Error("Invalid input type.");if(t.length>=3&&t[2].type==="string")throw new Error("Invalid input type.")},fb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),[u,n]=t.calculateTextureWidthAndHeight(e.dims,0),s=U.computeStrides(e.dims);switch(o.mode){case"constant":return cb(i,e.dims,s,u,n,o.pads,o.value);case"reflect":return pb(i,e.dims,s,u,n,o.pads);case"edge":return db(i,e.dims,s,u,n,o.pads);default:throw new Error("Invalid mode")}},cb=(t,e,o,i,u,n,s)=>{let d=e.length,f="";for(let r=d-1;r>=0;--r)f+=`
        k = m[${r}] - ${n[r]};
        if (k < 0)  return constant;
        if (k >= ${e[r]}) return constant;
        offset += k * ${o[r]};
        `;return`
      float padA(int m[${d}]) {
        const float constant = float(${s});
        int offset = 0;
        int k = 0;
        ${f}
        vec2 coords = offsetToCoords(offset, ${i}, ${u});
        float value = getColorAsFloat(${t.texture2D}(A, coords));
        return value;
      }
      `},pb=(t,e,o,i,u,n)=>{let s=e.length,d="";for(let f=s-1;f>=0;--f)d+=`
        k = m[${f}] - ${n[f]};
        if (k < 0) { k = -k; }
        {
          const int _2n_1 = ${2*(e[f]-1)};
          k = int( mod( float(k), float(_2n_1) ) ) ;
          if(k >= ${e[f]}) { k = _2n_1 - k; }
        }
        offset += k * ${o[f]};
        `;return`
      float padA(int m[${s}]) {
        int offset = 0;
        int k = 0;
        ${d}
        vec2 coords = offsetToCoords(offset, ${i}, ${u});
        float value = getColorAsFloat(${t.texture2D}(A, coords));
        return value;
      }
      `},db=(t,e,o,i,u,n)=>{let s=e.length,d="";for(let f=s-1;f>=0;--f)d+=`
        k = m[${f}] - ${n[f]};
        if (k < 0)  k = 0;
        if (k >= ${e[f]}) k = ${e[f]-1};
        offset += k * ${o[f]};
      `;return`
      float padA(int m[${s}]) {
        int offset = 0;
        int k = 0;
        ${d}
        vec2 coords = offsetToCoords(offset, ${i}, ${u});
        float value = getColorAsFloat(${t.texture2D}(A, coords));
        return value;
      }
      `}}),zf,Vf,Wf,Hf,qf,jf,Yf,Xf,Kf,hb,Uf,Jf,uo,Zf,so,mb,Qf=L(()=>{Ge(),fe(),ae(),zf=(t,e,o)=>{uo(e);let i={name:"AveragePool",inputNames:["X"],inputTypes:[0],cacheHint:o.cacheKey};return[t.run({...i,get:()=>Wf(e,i,!1,o)},e)]},Vf=t=>{let e=t.attributes.getString("auto_pad","NOTSET"),o=t.attributes.getInt("ceil_mode",0),i=t.attributes.getInt("count_include_pad",0)!==0,u=t.attributes.getInts("kernel_shape"),n=t.attributes.getInts("strides",[]),s=t.attributes.getInts("pads",[]);if(o!==0)throw new Error("using ceil() in shape computation is not yet supported for AveragePool");return ee({autoPad:e,ceilMode:o,countIncludePad:i,kernelShape:u,strides:n,pads:s})},Wf=(t,e,o,i)=>{let[u,n]=Kf(t,i,o),s=U.size(u.kernelShape),d="value += _X(x);",f="";u.countIncludePad?f+=`value /= float(${s});`:f+=`value /= float(${s} - pad);`;let r=`
        ${Zf(t[0].dims,u,d,f,"0.0")}
      `;return{...e,output:{dims:n,type:t[0].type,textureType:0},shaderSource:r}},Hf=(t,e,o)=>{uo(e);let i={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[0],cacheHint:`${o.countIncludePad}`};return[t.run({...i,get:()=>Wf(e,i,!0,o)},e)]},qf=t=>{let e=t.attributes.getInt("count_include_pad",0)!==0;return ee({autoPad:"",ceilMode:0,countIncludePad:e,kernelShape:[],strides:[],pads:[]})},jf=(t,e,o)=>{uo(e);let i={name:"MaxPool",inputNames:["X"],inputTypes:[0],cacheHint:o.cacheKey};return[t.run({...i,get:()=>Xf(e,i,!1,o)},e)]},Yf=t=>{let e=t.attributes.getString("auto_pad","NOTSET"),o=t.attributes.getInt("ceil_mode",0),i=t.attributes.getInts("kernel_shape"),u=t.attributes.getInts("strides",[]),n=t.attributes.getInts("pads",[]),s=t.attributes.getInt("storage_order",0),d=t.attributes.getInts("dilations",[]);if(s!==0)throw new Error("column major storage order is not yet supported for MaxPool");if(o!==0)throw new Error("using ceil() in shape computation is not yet supported for MaxPool");return ee({autoPad:e,ceilMode:o,countIncludePad:!1,kernelShape:i,strides:u,pads:n,storageOrder:s,dilations:d})},Xf=(t,e,o,i)=>{let[u,n]=Kf(t,i,o),s=`
      value = max(_X(x), value);
    `,d="",f=`
      ${Zf(t[0].dims,u,s,d,"-1e5")}
    `;return{...e,output:{dims:n,type:t[0].type,textureType:0},shaderSource:f}},Kf=(t,e,o)=>{let i=t[0].dims.slice(),u=Object.hasOwnProperty.call(e,"dilations"),n=e.kernelShape.slice(),s=e.strides.slice(),d=u?e.dilations.slice():[],f=e.pads.slice();pr.adjustPoolAttributes(o,i,n,s,d,f);let r=pr.computePoolOutputShape(o,i,s,d,n,f,e.autoPad),a=Object.assign({},e);return u?Object.assign(a,{kernelShape:n,strides:s,pads:f,dilations:d,cacheKey:e.cacheKey}):Object.assign(a,{kernelShape:n,strides:s,pads:f,cacheKey:e.cacheKey}),[a,r]},hb={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},Uf={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[0]},Jf=(t,e)=>(uo(e),[t.run({...Uf,get:()=>Xf(e,Uf,!0,hb)},e)]),uo=t=>{if(!t||t.length!==1)throw new Error("Pool ops requires 1 input.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type.")},Zf=(t,e,o,i,u)=>{let n=t.length;if(e.kernelShape.length<=2){let s=e.kernelShape[e.kernelShape.length-1],d=e.strides[e.strides.length-1],f=e.pads[e.pads.length/2-1],r=e.pads[e.pads.length-1],a=t[n-1],l="",p="",m="";if(f+r!==0?l=`
          for (int i = 0; i < ${s}; i++) {
            x[${n} - 1] = indices[${n} - 1] * ${d} - ${f} + i;
            if (x[${n} - 1] < 0 || x[${n} - 1] >= ${a}) {
              pad++;
              continue;
            }
            ${o}
          }`:l=`
          for (int i = 0; i < ${s}; i++) {
            x[${n} - 1] = indices[${n} - 1] * ${d} - ${f} + i;
            ${o}
          }`,e.kernelShape.length===2){let y=e.kernelShape[e.kernelShape.length-2],T=e.strides[e.strides.length-2],v=e.pads[e.pads.length/2-2],S=e.pads[e.pads.length-2],E=t[n-2];v+S!==0?p=`
            for (int j = 0; j < ${y}; j++) {
              x[${n} - 2] = indices[${n} - 2] * ${T} - ${v} + j;
              if (x[${n} - 2] < 0 || x[${n} - 2] >= ${E}) {
                pad+= ${s};
                continue;
              }
          `:p=`
            for (int j = 0; j < ${y}; j++) {
              x[${n} - 2] = indices[${n} - 2] * ${T} - ${v} + j;
            `,m=`
          }
        `}return`
        float process(int indices[${n}]) {
          int x[${n}];
          copyVec(indices, x);

          float value = ${u};
          int pad = 0;
          ${p}
          ${l}
          ${m}
          ${i}
          return value;
        }
      `}else{let s=U.size(e.kernelShape),d=U.computeStrides(e.kernelShape),f=d.length,r=e.pads.length,a=mb(f),l=so(t,"inputDims"),p=so(e.pads,"pads"),m=so(d,"kernelStrides"),y=so(e.strides,"strides"),T=e.pads.reduce((S,E)=>S+E),v="";return T?v=`
            if (x[j] >= inputDims[j] || x[j] < 0) {
              pad++;
              isPad = true;
              break;
            }
          }
          if (!isPad) {
            ${o}
          }`:v=`
          }
          ${o}
        `,`
        ${a}
        float process(int indices[${n}]) {
          int x[${n}];
          copyVec(indices, x);
          int offset[${f}];
          int pads[${r}];
          int inputDims[${n}];
          int kernelStrides[${f}];
          int strides[${f}];
          ${p}
          ${l}
          ${y}
          ${m}

          float value = ${u};
          int pad = 0;
          bool isPad = false;
          for (int i = 0; i < ${s}; i++) {
            offsetToIndices(i, kernelStrides, offset);
            isPad = false;
            for (int j = ${n} - ${f}; j < ${n}; j++) {
              x[j] = indices[j] * strides[j - ${n} + ${f}]
                + offset[j - ${n} + ${f}] - pads[j - 2];
              ${v}
          }
          ${i}

          return value;
        }
      `}},so=(t,e)=>{let o="";for(let i=0;i<t.length;i++)o+=`
      ${e}[${i}] = ${t[i]};
    `;return o},mb=t=>`
  void offsetToIndices(int offset, int[${t}] strides, out int[${t}] indices) {
    if (${t} == 0) {
      return;
    }
    for (int i = 0; i < ${t} - 1; ++i) {
      indices[i] = offset / strides[i];
      offset -= indices[i] * strides[i];
    }
    indices[${t} - 1] = offset;
  }`}),Tr,Zt,bb,gb,ec,tc,rc,nc,oc,ic,ac,sc=L(()=>{Ge(),pn(),fe(),ae(),Tr=(t,e,o,i,u)=>{gb(e);let n={name:i,inputNames:["A"],inputTypes:[0]};return[t.run({...n,cacheHint:o.cacheKey,get:()=>bb(t,e,o,i,u,n)},e)]},Zt=t=>{let e=t.attributes.getInts("axes",[]),o=t.attributes.getInt("keepdims",1)===1;return ee({axes:e,keepDims:o})},bb=(t,e,o,i,u,n)=>{let s=[],d=e[0].dims.length||1,f=[],r=U.normalizeAxes(o.axes,e[0].dims.length),a=u(e,r),l=a[1];for(let m=0;m<e[0].dims.length;m++)r.indexOf(m)>=0||r.length===0?(o.keepDims&&s.push(1),l=`
          for(int j${m} = 0; j${m} < ${e[0].dims[m]}; j${m}++) {
            inputIdx[${m}] = j${m};
            ${l}
          }`):(f.push(`inputIdx[${m}] = outputIdx[${s.length}];`),s.push(e[0].dims[m]));let p=`
      float process(int outputIdx[${s.length||1}]) {
        float value;                 // final result
        int inputIdx[${d}];      // addressing input data
        ${f.join(`
`)}
        ${a[0]}       // init ops for reduce max/min
        ${l}
        ${a[2]}       // final computation for reduce mean
        return value;
      }`;return{...n,output:{dims:s,type:e[0].type,textureType:0},shaderSource:p}},gb=t=>{if(!t||t.length!==1)throw new Error("Reduce op requires 1 input.");if(Jt.indexOf(t[0].type)===-1)throw new Error("Invalid input type.")},ec=(t,e,o)=>Tr(t,e,o,"ReduceSum",()=>["value = 0.0;","value += _A(inputIdx);",""]),tc=(t,e,o)=>Tr(t,e,o,"ReduceMean",(i,u)=>{let n=1;for(let s=0;s<i[0].dims.length;s++)(u.indexOf(s)>=0||u.length===0)&&(n*=i[0].dims[s]);return["value = 0.0;","value += _A(inputIdx);",`value /= ${n}.;`]}),rc=(t,e,o)=>Tr(t,e,o,"ReduceMax",(i,u)=>{let n=[];for(let s=0;s<i[0].dims.length;s++)(u.indexOf(s)>=0||u.length===0)&&n.push(`inputIdx[${s}] = 0;`);return[`${n.join(`
`)}
value = _A(inputIdx);`,"value = max(value, _A(inputIdx));",""]}),nc=(t,e,o)=>Tr(t,e,o,"ReduceMin",(i,u)=>{let n=[];for(let s=0;s<i[0].dims.length;s++)(u.indexOf(s)>=0||u.length===0)&&n.push(`inputIdx[${s}] = 0;`);return[`${n.join(`
`)}
value = _A(inputIdx);`,"value = min(value, _A(inputIdx));",""]}),oc=(t,e,o)=>Tr(t,e,o,"ReduceProd",()=>["value = 1.0;","value *= _A(inputIdx);",""]),ic=(t,e,o)=>Tr(t,e,o,"ReduceLogSum",()=>["value = 0.0;","value += _A(inputIdx);","value = log(value);"]),ac=(t,e,o)=>Tr(t,e,o,"ReduceLogSumSquare",()=>["float t; value = 0.0;","t = _A(inputIdx); value += t * t;",""])}),uc,lc=L(()=>{fe(),uc=(t,e)=>{let o=U.calculateReshapedDims(e[0].dims,e[1].integerData);return t.session.pack?[t.reshapePacked(e[0],o)]:[t.reshapeUnpacked(e[0],o)]}}),fc,Ni,cc,pc,dn,yb,Ri,lo,Mi=L(()=>{Ge(),xe(),ae(),fc={name:"Upsample",inputNames:["X"],inputTypes:[0]},Ni=(t,e,o)=>(Ri(e,o),[t.run({...fc,cacheHint:o.cacheKey,get:()=>yb(t,e,o)},e)]),cc=t=>dn(t,7),pc=t=>dn(t,9),dn=(t,e)=>{let o=e>=10,i=t.attributes.getString("mode","nearest");if(i!=="nearest"&&i!=="linear"&&(e<11||i!=="cubic"))throw new Error(`unrecognized mode: ${i}`);let u=[];e<9&&(u=t.attributes.getFloats("scales"),lo(u,i,o));let n=t.attributes.getFloat("extrapolation_value",0),s=e>10?t.attributes.getString("coordinate_transformation_mode","half_pixel"):"asymmetric";if(["asymmetric","pytorch_half_pixel","tf_half_pixel_for_nn","align_corners","tf_crop_and_resize","half_pixel"].indexOf(s)===-1)throw new Error(`coordinate_transform_mode '${s}' is not supported`);let d=s==="tf_crop_and_resize",f=d,r=i==="nearest"&&e>=11?t.attributes.getString("nearest_mode","round_prefer_floor"):"";if(["round_prefer_floor","round_prefer_ceil","floor","ceil",""].indexOf(r)===-1)throw new Error(`nearest_mode '${r}' is not supported`);let a=t.attributes.getFloat("cubic_coeff_a",-.75),l=t.attributes.getInt("exclude_outside",0)!==0;if(l&&i!=="cubic")throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");let p=e<11?!0:i==="nearest"&&s==="asymmetric"&&r==="floor",m=0,y=0,T=0;return e>10?t.inputs.length>2?(m=1,y=2,T=3):(y=1,T=2):e===9&&(y=1),ee({opset:e,isResize:o,mode:i,scales:u,extrapolationValue:n,coordinateTransformMode:s,useExtrapolation:f,needRoiInput:d,nearestMode:r,cubicCoefficientA:a,excludeOutside:l,useNearest2xOptimization:p,roiInputIdx:m,scalesInputIdx:y,sizesInputIdx:T})},yb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),[u,n]=t.calculateTextureWidthAndHeight(e[0].dims,0),s=e[0].dims.map((T,v)=>Math.floor(T*o.scales[v])),[d,f]=t.calculateTextureWidthAndHeight(s,0),r=s.length,a=new Array(r),l=new Array(r),p=`
      int output_pitches[${r}];
      int input_pitches[${r}];
      `;for(let T=r-1;T>=0;T--)a[T]=T===r-1?1:a[T+1]*s[T+1],l[T]=T===r-1?1:l[T+1]*e[0].dims[T+1],p+=`
        output_pitches[${T}] = ${a[T]};
        input_pitches[${T}] = ${l[T]};
        `;let m=`
      float getInputFloat(int index) {
        vec2 coords = offsetToCoords(index, ${u}, ${n});
        float value = getColorAsFloat(${i.texture2D}(X, coords));
        return value;
      }
      `,y=o.mode==="nearest"?`
    ${m}
    float process(int indices[${r}]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${d}, ${f});

      ${p}

      int d, m;
      for (int dim = 0; dim < ${r}; ++dim) {
        d = output_index / output_pitches[dim];
        m = output_index - d * output_pitches[dim];
        output_index = m;

        if (scales[dim] != 1 && d > 0) {
          int d2 = d / scales[dim];
          m = d - d2 * scales[dim];
          d = d2;
        }
        input_index += input_pitches[dim] * d;
      }

      return getInputFloat(input_index);
    }`:r===4?`
    ${m}
    float process(int indices[4]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${d}, ${f});

      ${p}

      int m;
      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
      index_of_dim0 = output_index / output_pitches[0];
      m = output_index - index_of_dim0 * output_pitches[0];
      index_of_dim1 = m / output_pitches[1];
      m = m - index_of_dim1 * output_pitches[1];
      index_of_dim2 = m / output_pitches[2];
      m = m - index_of_dim2 * output_pitches[2];
      index_of_dim3 = m;

      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
      index_of_input_dim2 = index_of_dim2 / scales[2];
      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
      index_of_input_dim3 = index_of_dim3 / scales[3];
      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];

      input_index = index_of_dim0 * input_pitches[0] +
            index_of_dim1 * input_pitches[1] +
            index_of_input_dim2 * input_pitches[2] +
            index_of_input_dim3;

      float x00 = getInputFloat(input_index);
      float x10, x01, x11;

      bool end_of_dim2 = false;
      if (index_of_input_dim2 == (${e[0].dims[2]} - 1)) {
        // It's the end in dimension 2
        x01 = x00;
        end_of_dim2 = true;
      } else {
        x01 = getInputFloat(input_index + input_pitches[2]);
      }

      if (index_of_input_dim3 == (input_pitches[2] - 1)) {
        // It's the end in dimension 3
        x10 = x00;
        x11 = x01;
      }
      else {
        x10 = getInputFloat(input_index + 1);
        x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
      }

      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
      return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
    }`:`
    ${m}
    float process(int indices[2]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${d}, ${f});

      ${p}

      int m;
      int index_of_dim0, index_of_dim1;
      index_of_dim0 = output_index / output_pitches[0];
      m = output_index - index_of_dim0 * output_pitches[0];
      index_of_dim1 = m;

      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
      index_of_input_dim0 = index_of_dim0 / scales[0];
      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
      index_of_input_dim1 = index_of_dim1 / scales[1];
      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];

      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;

      float x00 = getInputFloat(input_index);
      float x10, x01, x11;

      bool end_of_dim0 = false;
      if (index_of_input_dim0 == (${e[0].dims[0]} - 1)) {
        // It's the end in dimension 0
        x01 = x00;
        end_of_dim0 = true;
      } else {
        x01 = getInputFloat(input_index + input_pitches[0]);
      }

      if (index_of_input_dim1 == (input_pitches[0] - 1)) {
        // It's the end in dimension 1
        x10 = x00;
        x11 = x01;
      }
      else {
        x10 = getInputFloat(input_index + 1);
        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
      }

      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
    }`;return{...fc,output:{dims:s,type:e[0].type,textureType:0},shaderSource:y,variables:[{name:"scales",type:"int",arrayLength:o.scales.length,data:o.scales.map(T=>Math.ceil(T))}]}},Ri=(t,e)=>{if(!t||e.opset<9&&t.length!==1||e.opset>=9&&e.opset<11&&t.length!==2||e.opset>=11&&t.length<2)throw new Error("invalid inputs.");if(e.scales.length>0&&t[0].dims.length!==e.scales.length)throw new Error("Invalid input shape.");if(t[0].type==="string")throw new Error("Invalid input tensor types.")},lo=(t,e,o)=>{if(o){for(let i of t)if(i<=0)throw new Error("Scale value should be greater than 0.")}else for(let i of t)if(i<1)throw new Error("Scale value should be greater than or equal to 1.");if((e==="linear"||e==="cubic")&&t.length!==2&&(t.length!==4||t[0]!==1||t[1]!==1))throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic')         or 4-D inputs with the corresponding outermost 2 scale values being 1         in the ${o?"Resize":"Upsample"} opeartor.`)}}),Gi,Ui,dc,hc,Tb,xb,vb,wb,mc=L(()=>{xe(),ae(),Ft(),br(),Mi(),Gi={name:"Resize",inputNames:["A"],inputTypes:[2]},Ui=(t,e,o)=>(Ri(e,o),[t.run({...Gi,cacheHint:o.cacheKey,get:()=>Tb(t,e,o)},e)]),dc=t=>dn(t,10),hc=t=>dn(t,11),Tb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),[u,n]=xb(e,o);if(u.every(E=>E===1)&&o.coordinateTransformMode!=="tf_crop_and_resize")return{...Gi,output:{dims:n,type:e[0].type,textureType:2},hasMain:!0,shaderSource:`void main() {
                    vec4 v = ${i.texture2D}(X, TexCoords);
                    ${i.output} = v;
                }`};let s=n.length;if(s<2)throw new Error(`output dimension should be at least 2, but got ${s}`);let d=n[s-2],f=n[s-1],r=e[0].dims;if(s!==r.length)throw new Error(`output dimension should match input ${r.length}, but got ${s}`);let a=r[s-2],l=r[s-1],p=u[s-2],m=u[s-1],y="";if(o.mode!=="linear")throw new Error(`resize (packed) does not support mode: '${o.mode}'`);switch(o.coordinateTransformMode){case"asymmetric":y=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return vec4(coords) / scaleWHWH;
                    }
                `;break;case"half_pixel":y=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
                    }
                `;break;case"pytorch_half_pixel":y=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 fcoords = vec4(coords);
                        return vec4(
                            ${f}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
                            ${d}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
                            ${f}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
                            ${d}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
                          );
                    }
                `;break;case"align_corners":y=`
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 resized = vec4(${f}.0 - 1.0, ${d}.0 - 1.0, ${f}.0 - 1.0,
                            ${d}.0 - 1.0);
                        vec4 original = vec4(${l}.0 - 1.0, ${a}.0 - 1.0, ${l}.0 - 1.0,
                            ${a}.0 - 1.0);
                        vec4 new_scale = original / resized;
                        return vec4(coords) * new_scale;
                    }
                `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${o.coordinateTransformMode}'`)}let T=Ke(s),v=$t(),S=`
            const vec2 inputWH = vec2(${a}.0, ${l}.0);
            const vec4 scaleWHWH = vec4(float(${p}), float(${m}), float(${p}), float(${m}));
            ${v}
            ${y}
            float getAValue(int x10, int r, int c, int d) {
                return getChannel(getA(x10, r, c, d), vec2(c, d));
            }
            void main() {
                ${T} rc = getOutputCoords();

                int batch = rc[0];
                int depth = rc[1];

                // retrieve the 4 coordinates that is used in the 4 packed output values.
                ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);

                // calculate the source index in fraction
                vec4 sourceFrac = getSourceFracIndex(coords);

                // get the lower and upper bound of the 4 values that will be packed into one texel.
                ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));
                ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));
                ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));
                ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));

                bool hasNextRow = rc.w < ${d-1};
                bool hasNextCol = rc.z < ${f-1};

                // pack x00, x01, x10, x11's top-left corner into one vec4 structure
                vec4 topLeft = vec4(
                    getAValue(batch, depth, x00.x, x00.y),
                    hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);

                // pack x00, x01, x10, x11's top-right corner into one vec4 structure
                vec4 topRight = vec4(
                    getAValue(batch, depth, x00.x, x00.w),
                    hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);

                // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure
                vec4 bottomLeft = vec4(
                    getAValue(batch, depth, x00.z, x00.y),
                    hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);

                // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure
                vec4 bottomRight = vec4(
                    getAValue(batch, depth, x00.z, x00.w),
                    hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);

                // calculate the interpolation fraction on u and v direction
                vec4 frac = vec4(sourceFrac) - floor(sourceFrac);
                vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));

                vec4 top = mix(topLeft, topRight, clampFrac.ywyw);
                vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);
                vec4 newValue = mix(top, bottom, clampFrac.xxzz);

                ${i.output} = vec4(newValue);
            }
        `;return{...Gi,output:{dims:n,type:e[0].type,textureType:2},hasMain:!0,shaderSource:S}},xb=(t,e)=>{let o=t[0].dims,i=e.scales,u;if(i.length===0){let s=t[e.scalesInputIdx];if(s&&s.size!==0){if(t[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");i=vb(s,e.mode,e.isResize)}else{let d=t[e.sizesInputIdx];if(!d||d.size===0)throw new Error("Either scales or sizes MUST be provided as input.");u=Array.from(d.integerData),i=wb(u,o,e.mode,e.isResize)}}else if(t[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");let n=u||o.map((s,d)=>Math.floor(s*i[d]));return[i,n]},vb=(t,e,o)=>{let i=Array.from(t.floatData);return lo(i,e,o),i},wb=(t,e,o,i)=>{let u=e.length,n=new Array(u);for(let s=0,d=u;s<d;s++)if(e[s]===0){if(t[s]!==0)throw new Error("Input dim is zero but required output dim is non-zero.");n[s]=1}else n[s]=t[s]/e[s];return lo(n,o,i),n}}),bc,_b,gc=L(()=>{mr(),bc=(t,e)=>(_b(e),[new Fe([e[0].dims.length],"int32",void 0,void 0,new Int32Array(e[0].dims))]),_b=t=>{if(!t||t.length!==1)throw new Error("Shape requires 1 input.")}}),zi,yc,Tc,xc,Ob,vc,Ib,Sb,wc=L(()=>{Ge(),pn(),fe(),ae(),zi={name:"Slice",inputNames:["A"],inputTypes:[0]},yc=(t,e,o)=>(Ob(e),[t.run({...zi,cacheHint:o.cacheKey,get:()=>xc(t,e[0],o)},e)]),Tc=t=>{let e=t.attributes.getInts("starts"),o=t.attributes.getInts("ends"),i=t.attributes.getInts("axes",[]);return ee({starts:e,ends:o,axes:i})},xc=(t,e,o)=>{let i=o.axes.length===0?e.dims.slice(0).map((a,l)=>l):o.axes,u=U.normalizeAxes(i,e.dims.length),n=o.starts.map((a,l)=>a>e.dims[u[l]]-1?e.dims[u[l]]:U.normalizeAxis(a,e.dims[u[l]])),s=o.ends.map((a,l)=>a>e.dims[u[l]]-1?e.dims[u[l]]:U.normalizeAxis(a,e.dims[u[l]])),d=e.dims.slice(),f=[];for(let a=0;a<u.length;a++)d[u[a]]=s[a]-n[a],n[a]>0&&f.push(`outputIdx[${u[a]}] += ${n[a]};`);let r=`
      float process(int outputIdx[${d.length}]) {
        ${f.join(`
      `)}
        return _A(outputIdx);
      }`;return{...zi,output:{dims:d,type:e.type,textureType:0},shaderSource:r}},Ob=t=>{if(!t||t.length!==1)throw new Error("Slice requires 1 input.");if(Jt.indexOf(t[0].type)===-1)throw new Error("Invalid input type.")},vc=(t,e)=>{Sb(e);let o=Ib(t,e);return[t.run({...zi,cacheHint:o.cacheKey,get:()=>xc(t,e[0],o)},[e[0]])]},Ib=(t,e)=>{if(!t.session.isInitializer(e[1].dataId)||!t.session.isInitializer(e[2].dataId)||e.length>=4&&!t.session.isInitializer(e[3].dataId)||e.length>=5&&!t.session.isInitializer(e[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(e.length>=5&&e[4].integerData.some(s=>s!==1))throw new Error("currently non-1 steps is not supported for Slice");let o=Array.from(e[1].integerData),i=Array.from(e[2].integerData),u=e.length>=4?Array.from(e[3].integerData):[],n=`${u};${o};${i}`;return{starts:o,ends:i,axes:u,cacheKey:n}},Sb=t=>{if(!t||t.length<3||t.length>5)throw new Error("Invalid input number.");if(t[1].type!=="int32"||t[1].dims.length!==1)throw new Error("Invalid input type.");if(t[2].type!=="int32"||t[2].dims.length!==1)throw new Error("Invalid input type.");if(t.length>=4&&(t[3].type!=="int32"||t[3].dims.length!==1))throw new Error("Invalid input type.");if(t.length>=5&&(t[4].type!=="int32"||t[4].dims.length!==1))throw new Error("Invalid input type.")}}),_c,Oc,Ic,Sc,Ac,Pc,Ec,Dc,Ab,Pb,Eb,Lc,Fc=L(()=>{Ge(),fe(),xe(),ae(),ao(),_c={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[0]},Oc={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[0,0]},Ic={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[0,0,0]},Sc=(t,e,o)=>{Lc(e);let i=e[0].dims.slice(),u=U.normalizeAxis(o.axis,i.length),n=U.sizeToDimension(i,u),s=U.sizeFromDimension(i,u);return Dc(t,e,o,n,s)},Ac=t=>ee({axis:t.attributes.getInt("axis",1)}),Pc=t=>ee({axis:t.attributes.getInt("axis",-1)}),Ec=(t,e,o)=>{Lc(e);let i=e[0].dims.slice(),u=U.normalizeAxis(o.axis,i.length),n=i.length,s=u!==n-1,d=[],f=[],r=[],a;s&&(f=Array.from({length:n}).map((y,T)=>T),f[u]=n-1,f[n-1]=u,f.map(y=>d.push(i[y])),a=ee({perm:f}),r=yr(t,e,a));let l=s?U.sizeToDimension(d,n-1):U.sizeToDimension(i,n-1),p=s?U.sizeFromDimension(d,n-1):U.sizeFromDimension(i,n-1),m=Dc(t,s?r:e,o,l,p);return s?yr(t,m,a):m},Dc=(t,e,o,i,u)=>{let n=Ab(t,e[0],i,u,[i]),s=t.run({..._c,cacheHint:o.cacheKey,get:()=>n},e),d=Pb(t,e[0],i,u,n.output.dims,[i]),f=t.run({...Oc,cacheHint:o.cacheKey,get:()=>d},[e[0],s]),r=Eb(t,e[0],i,u,n.output.dims,d.output.dims);return[t.run({...Ic,cacheHint:o.cacheKey,get:()=>r},[e[0],s,f])]},Ab=(t,e,o,i,u)=>{let[n,s]=t.calculateTextureWidthAndHeight(e.dims,0),d=u.length;if(o<1||i<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(u.length!==1)throw new Error("Dimensionality of the output should be 1");if(u[0]!==o)throw new Error("Shape of the output should be equal to logical row count");let f=q(t.session.backend.glContext.version),r=`
      float process(int[${d}] indices) {
        int logical_row_start_offset = indices[0] * ${i};

        float max = getColorAsFloat(${f.texture2D}(A, offsetToCoords(logical_row_start_offset, ${n},
        ${s} )));
        for(int i=1; i<${i}; ++i)
        {
          float current = getColorAsFloat(${f.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${n}, ${s})));
          if(current > max)
          max = current;
        }

        return max;
      }`;return{..._c,output:{dims:u,type:e.type,textureType:0},shaderSource:r}},Pb=(t,e,o,i,u,n)=>{let[s,d]=t.calculateTextureWidthAndHeight(e.dims,0),f=n.length;if(o<1||i<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(n.length!==1)throw new Error("Dimensionality of the output should be 1");if(n[0]!==o)throw new Error("Shape of the output should be equal to logical row count");if(u.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(u[0]!==o)throw new Error("Shape of the intermediate results should be equal to logical row count");let r=q(t.session.backend.glContext.version),a=`
      float process(int[${f}] indices) {
        int logical_row_start_offset = indices[0] * ${i};

        float norm_factor = 0.0;
        float max = _Max(indices);
        for(int i=0; i<${i}; ++i)
        {
          norm_factor += exp(getColorAsFloat(${r.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${s}, ${d}))) - max);
        }

        return norm_factor;
      }`;return{...Oc,output:{dims:n,type:e.type,textureType:0},shaderSource:a}},Eb=(t,e,o,i,u,n)=>{let[s,d]=t.calculateTextureWidthAndHeight(e.dims,0),f=e.dims.length;if(o<1||i<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(u.length!==1||n.length!==1)throw new Error("Dimensionality of the intermediate results should be 1");if(u[0]!==o||n[0]!==o)throw new Error("Shape of the intermediate results should be equal to logical row count");let r=`
      float process(int[${f}] indices) {

      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
      int offset = coordsToOffset(TexCoords, ${s}, ${d});

      //determine the logical row for this index
      int logical_row_index[1];
      logical_row_index[0] = offset / ${i};

      float norm_factor = _Norm(logical_row_index);

      // avoid possible division by 0
      // if norm_facor is 0, all elements are zero
      // if so, return 0
      if(norm_factor == 0.0)
        return 0.0;

      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
    }`;return{...Ic,output:{dims:e.dims,type:e.type,textureType:0},shaderSource:r}},Lc=t=>{if(!t||t.length!==1)throw new Error("Softmax requires 1 input.");if(t[0].type!=="float32"&&t[0].type!=="float64")throw new Error("Invalid input type")}}),$c,Cc,kc,Db,Lb,Fb,Bc=L(()=>{Ge(),fe(),ae(),$c={name:"Split",inputNames:["A"],inputTypes:[0]},Cc=(t,e,o)=>{Fb(e);let i=U.normalizeAxis(o.axis,e[0].dims.length),u=Db(t,e,i,o),n=[];for(let s=0;s<u;++s)n.push(t.run({...$c,cacheHint:`${o.cacheKey};${s}`,get:()=>Lb(t,e[0],o,i,s)},e));return n},kc=t=>{let e=t.attributes.getInt("axis",0),o=t.attributes.getInts("split",[]),i=t.outputs.length;return ee({axis:e,split:o,numOutputs:i})},Db=(t,e,o,i)=>{let[,u]=an.splitShape(e[0].dims,o,i.split,i.numOutputs);return u.length},Lb=(t,e,o,i,u)=>{let[n,s]=an.splitShape(e.dims,i,o.split,o.numOutputs),d=s[u],f=n[u],r=`
      float process(int indices[${f.length}]) {
        indices[${i}] += ${d};
        return _A(indices);
      }
    `;return{...$c,cacheHint:`${o.cacheKey}:${u}`,output:{dims:f,type:e.type,textureType:0},shaderSource:r}},Fb=t=>{if(!t||t.length!==1)throw new Error("Split requires one input.");if(t[0].type!=="int8"&&t[0].type!=="uint8"&&t[0].type!=="int16"&&t[0].type!=="uint16"&&t[0].type!=="int32"&&t[0].type!=="uint32"&&t[0].type!=="float32"&&t[0].type!=="float64"&&t[0].type!=="bool")throw new Error("Invalid input type.")}}),Vi,Nc,Rc,$b,Cb,Mc=L(()=>{fe(),Vi=(t,e,o)=>{$b(e);let i=U.squeezeShape(e[0].dims,o);return[t.reshapeUnpacked(e[0],i)]},Nc=(t,e)=>(Cb(e),Vi(t,[e[0]],Array.from(e[1].integerData))),Rc=t=>t.attributes.getInts("axes"),$b=t=>{if(!t||t.length!==1)throw new Error("Squeeze requires 1 input.");if(t[0].type==="string")throw new Error("invalid input tensor types.")},Cb=t=>{if(!t||t.length!==2)throw new Error("Squeeze requires 2 inputs.");if(t[1].type!=="int32")throw new Error("Invalid input type.")}}),Gc,kb,Bb,Uc=L(()=>{xe(),ae(),Gc=(t,e)=>{Bb(e);let o={name:"Sum",inputNames:e.map((i,u)=>`X${u}`),inputTypes:new Array(e.length).fill(0)};return[t.run({...o,get:()=>kb(t,e,o)},e)]},kb=(t,e,o)=>{let i=q(t.session.backend.glContext.version),u=e[0].dims.slice(),n=`
      void main() {
        vec4 result = ${e.map((s,d)=>`${i.texture2D}(X${d},TexCoords)`).join(" + ")};
        ${i.output} = result;
      }
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        ivec3 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
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      ivec4 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
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          int index = resTexRC.y * ${e[0]} + resTexRC.x;
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        }
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      ivec5 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
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          int index = resTexRC.y * ${e[0]} + resTexRC.x;
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        }
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     ivec6 getOutputCoords() {
         ivec2 resTexRC = ivec2(TexCoords.xy *
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         int index = resTexRC.y * ${e[0]} + resTexRC.x;
         ${s}
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       }
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      int texR = index - texC * texNumR;
      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
      //       v.
      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
    }
    `),e="packedUVfrom1D",t[e]=new R(`
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        int texelIndex = index / 2;
        int texR = texelIndex / texNumC;
        int texC = texelIndex - texR * texNumC;
        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
      }
      `),e="packedUVfrom2D",t[e]=new R(`
      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
        int texR = texelIndex / texNumC;
        int texC = texelIndex - texR * texNumC;
        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
      }
      `),e="packedUVfrom3D",t[e]=new R(`
      vec2 packedUVfrom3D(int texNumR, int texNumC,
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          int row, int col) {
        int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
        int texR = index / texNumC;
        int texC = index - texR * texNumC;
        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
      }
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        return vec4(outputValue.xy, outputValue.xy);
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          return vec4(outputValue.x, outputValue.x, 0., 0.);
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          return vec4(outputValue.x);
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        int lastDim = coords.${m[f-1]};
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new Error("duplicated attribute names")}}set(e,o,i){this._attributes.set(e,[i,o])}delete(e){this._attributes.delete(e)}getFloat(e,o){return this.get(e,"float",o)}getInt(e,o){return this.get(e,"int",o)}getString(e,o){return this.get(e,"string",o)}getTensor(e,o){return this.get(e,"tensor",o)}getFloats(e,o){return this.get(e,"floats",o)}getInts(e,o){return this.get(e,"ints",o)}getStrings(e,o){return this.get(e,"strings",o)}getTensors(e,o){return this.get(e,"tensors",o)}get(e,o,i){let u=this._attributes.get(e);if(u===void 0){if(i!==void 0)return i;throw new Error(`required attribute not found: ${e}`)}if(u[1]!==o)throw new Error(`type mismatch: expected ${o} but got ${u[1]}`);return u[0]}static getType(e){let o=e instanceof oe.onnx.AttributeProto?e.type:e.type();switch(o){case oe.onnx.AttributeProto.AttributeType.FLOAT:return"float";case oe.onnx.AttributeProto.AttributeType.INT:return"int";case oe.onnx.AttributeProto.AttributeType.STRING:return"string";case oe.onnx.AttributeProto.AttributeType.TENSOR:return"tensor";case oe.onnx.AttributeProto.AttributeType.FLOATS:return"floats";case oe.onnx.AttributeProto.AttributeType.INTS:return"ints";case oe.onnx.AttributeProto.AttributeType.STRINGS:return"strings";case oe.onnx.AttributeProto.AttributeType.TENSORS:return"tensors";default:throw new Error(`attribute type is not supported yet: ${oe.onnx.AttributeProto.AttributeType[o]}`)}}static getValue(e){let o=e instanceof oe.onnx.AttributeProto?e.type:e.type();if(o===oe.onnx.AttributeProto.AttributeType.GRAPH||o===oe.onnx.AttributeProto.AttributeType.GRAPHS)throw new Error("graph attribute is not supported yet");let i=this.getValueNoCheck(e);if(o===oe.onnx.AttributeProto.AttributeType.INT&&nt.isLong(i))return nt.longToNumber(i);if(o===oe.onnx.AttributeProto.AttributeType.INTS){let u=i,n=new Array(u.length);for(let s=0;s<u.length;s++){let d=u[s];n[s]=nt.longToNumber(d)}return n}if(o===oe.onnx.AttributeProto.AttributeType.TENSOR)return e instanceof 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o}default:throw new Error(`unsupported attribute type: ${mt.AttributeType[e.type()]}`)}}}}),Ji,_o,Zi,kt,Oo,Ki,gp=L(()=>{bp(),tn(),Ji=Er($r()),mr(),fe(),_o=W.experimental.fbs,Zi={from:(t,e)=>new Ki(t,e)},kt=class{constructor(t){this._from=void 0,this._to=[],this.tensor=void 0,this.type=void 0,t&&(this.type=Ve.tensorValueTypeFromProto(t.type.tensorType))}get from(){return this._from}get to(){return this._to}},Oo=class{constructor(t,e){t instanceof Ji.onnx.NodeProto?(this.name=t.name,this.opType=t.opType,this.attributes=new mn(t.attribute)):t instanceof _o.Node&&(this.name=e??t.name(),this.opType=t.opType(),this.attributes=new mn(Ve.tensorAttributesFromORTFormat(t))),this.inputs=[],this.outputs=[],this.executeNode=!0}},Ki=class{constructor(t,e){if(!t)throw new TypeError("graph is empty");this.buildGraph(t),this.transformGraph(e),this.checkIsAcyclic()}getInputIndices(){return this._allInputIndices}getInputNames(){return this._allInputNames}getOutputIndices(){return this._allOutputIndices}getOutputNames(){return this._allOutputNames}getValues(){return this._allData}getNodes(){return this._nodes}buildGraph(t){if(t instanceof Ji.onnx.GraphProto)this.buildGraphFromOnnxFormat(t);else if(t instanceof _o.Graph)this.buildGraphFromOrtFormat(t);else throw new TypeError("Graph type is not supported.")}buildGraphFromOnnxFormat(t){let e=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let o=new Map;if(!t.input)throw new Error("missing information in graph: input");let i=[];for(let u of t.input){if(e.has(u.name))throw new Error(`duplicated input name: ${u.name}`);let n=this._allData.push(new kt(u))-1;e.set(u.name,n),i.push(u.name)}if(!t.initializer)throw new Error("missing information in graph: initializer");for(let u of t.initializer){let n=e.get(u.name);if(n===void 0){let s=new kt;s.type={shape:{dims:Ve.tensorDimsFromProto(u.dims)},tensorType:Ve.tensorDataTypeFromProto(u.dataType)},n=this._allData.push(s)-1,e.set(u.name,n)}this._allData[n]._from=-1,this._allData[n].tensor=Fe.fromProto(u)}for(let u=0;u<this._allData.length;u++)this._allData[u].tensor||(this._allInputIndices.push(u),this._allInputNames.push(i[u]));if(!t.output)throw new Error("missing information in graph: output");for(let u of t.output){if(e.has(u.name))throw new Error(`duplicated output name: ${u.name}`);let n=this._allData.push(new kt(u))-1;e.set(u.name,n),this._allOutputIndices.push(n),this._allOutputNames.push(u.name)}if(!t.node)throw new Error("missing information in graph: node");for(let u of t.node){if(!u.name)for(let s=0;;s++){let d=`unnamed_${u.opType}_${s}`;if(!o.has(d)){u.name=d;break}}if(o.has(u.name))throw new Error(`duplicated node name: ${u.name}`);let n=this._nodes.push(new Oo(u))-1;o.set(u.name,n)}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.node[u];if(!s.output)throw new Error(`missing output for node: ${s.name}`);for(let d of s.output){let f=e.get(d);if(typeof f>"u"&&(f=this._allData.push(new kt)-1,e.set(d,f)),n.outputs.push(f),this._allData[f]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${f}`);if(this._allData[f]._from=u,s.opType==="Constant"){if(!s.attribute||s.attribute.length!==1||!s.attribute[0].t)throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(!s.output||s.output.length!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");n.outputs.pop(),n.executeNode=!1,this._allData[f]._from=-1,this._allData[f].tensor=Fe.fromProto(s.attribute[0].t)}}}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.node[u];if(!s.input)throw new Error(`missing input for node: ${s.name}`);for(let d of s.input){let f=e.get(d);if(typeof f>"u"){if(d===""&&(s.input.length===3||s.input.length===4)&&s.opType==="Resize")continue;throw new Error(`unrecognized input '${d}' for node: ${s.name}`)}n.inputs.push(f),this._allData[f]._to.push(u)}}return!0}buildGraphFromOrtFormat(t){let e=new Map;this._allData=[],this._allInputIndices=[],this._allInputNames=[],this._allOutputIndices=[],this._allOutputNames=[],this._nodes=[];let o=new Map,i=[];for(let u=0;u<t.inputsLength();u++){let n=t.inputs(u);if(e.has(n))throw new Error(`duplicated input name: ${n}`);for(let s=0;s<t.nodeArgsLength();s++)if(t.nodeArgs(s)?.name()===n){let d=new kt;if(t.nodeArgs(s)?.type()?.valueType()!==_o.TypeInfoValue.tensor_type)throw new Error("Unexpected value type for the nodeArg.");let f=t.nodeArgs(s).type().value(new _o.TensorTypeAndShape),r=Ve.tensorDataTypeFromProto(f.elemType()),a=f.shape(),l=[];for(let m=0;m<a.dimLength();m++)l.push(nt.longToNumber(a.dim(m).value().dimValue()));d.type={shape:{dims:l},tensorType:r};let p=this._allData.push(d)-1;e.set(n,p),i.push(n)}}for(let u=0;u<t.initializersLength();u++){let n=t.initializers(u),s=e.get(n.name());if(s===void 0){let d=new kt,f=Ve.tensorDimsFromORTFormat(n),r=Ve.tensorDataTypeFromProto(n.dataType());d.type={shape:{dims:f},tensorType:r},s=this._allData.push(d)-1,e.set(n.name(),s)}this._allData[s]._from=-1,this._allData[s].tensor=Fe.fromOrtTensor(n)}for(let u=0;u<this._allData.length;u++)this._allData[u].tensor||(this._allInputIndices.push(u),this._allInputNames.push(i[u]));for(let u=0;u<t.outputsLength();u++){let n=t.outputs(u);if(e.has(n))throw new Error(`duplicated output name: ${n}`);let s=this._allData.push(new kt)-1;e.set(n,s),this._allOutputIndices.push(s),this._allOutputNames.push(n)}if(!t.nodes)throw new Error("missing information in graph: node");for(let u=0;u<t.nodesLength();u++){let n=t.nodes(u),s=n.name();if(!s)for(let f=0;s=`unnamed_${n.opType()}_${f}`,!!o.has(s);f++);if(o.has(s))throw new Error(`duplicated node name: ${s}`);let d=this._nodes.push(new Oo(n,s))-1;o.set(s,d)}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.nodes(u);if(s==null)throw new Error(`No node exists at index ${u}`);if(s?.outputsLength()===0)throw new Error(`missing output for node: ${s.name}`);for(let d=0;d<s?.outputsLength();d++){let f=s?.outputs(d),r=e.get(f);if(typeof r>"u"&&(r=this._allData.push(new kt)-1,e.set(f,r)),n.outputs.push(r),this._allData[r]._from!==void 0)throw new Error(`multiple nodes output to one data value: ${r}`);if(this._allData[r]._from=u,s.opType()==="Constant"){if(s.attributesLength()!==1||!s.attributes(0).t())throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");if(s.outputsLength()!==1)throw new Error("missing output or incorrect number of outputs for this Constant operator");n.outputs.pop(),n.executeNode=!1,this._allData[r]._from=-1,this._allData[r].tensor=Fe.fromOrtTensor(s.attributes(0).t())}}}for(let u=0;u<this._nodes.length;u++){let n=this._nodes[u],s=t.nodes(u);if(s.inputsLength()===0)throw new Error(`missing input for node: ${s.name}`);for(let d=0;d<s.inputsLength();d++){let f=s.inputs(d),r=e.get(f);if(typeof r>"u")throw new Error(`unrecognized input '${f}' for node: ${s.name()}`);n.inputs.push(r),this._allData[r]._to.push(u)}}}checkIsAcyclic(){let t=new Set;this._allInputIndices.forEach(i=>{this._allData[i]._to.forEach(u=>{t.add(u)})});let e=Array.from(t),o=new Array(this._nodes.length).fill("white");for(;e.length>0;){let i=e.pop();o[i]==="gray"?o[i]="black":(e.push(i),o[i]="gray",this._nodes[i].outputs.forEach(u=>{let n=this._allData[u];if(typeof n.tensor<"u")throw new Error("node outputs should not be initialized");if(n._from!==i)throw new Error("from property of the Value object doesn't match index of Node being processed");n._to.forEach(s=>{if(o[s]==="gray")throw new Error("model graph is cyclic");o[s]==="white"&&e.push(s)})}))}}transformGraph(t){this.removeAllIdentityNodes(),this.removeAllDropoutNodes(),this.fuseConvActivationNodes(),t&&t.transformGraph(this),this.finalizeGraph()}finalizeGraph(){let t=0,e=new Array(this._nodes.length,0),o=0;for(let i=0;i<this._nodes.length;i++)e[i]=o,this._nodes[i].executeNode?(o!==i&&(this._nodes[o]=this._nodes[i]),o++):this._nodes[i].outputs.forEach(u=>{this._allData[u]._from=-2});this._nodes.splice(o,this._nodes.length-o);for(let i=0;i<this._allData.length;i++){let u=this._allData[i];u._from!==void 0&&u._from!==-1&&u._from!==-2&&(u._from=e[u._from]);for(let n=0;n<u._to.length;n++)if(u._to[n]>=0)u._to[n]=e[u._to[n]];else throw new Error("Trying to update a removed node")}t=0;for(let i=0;i<this._allData.length;i++){if(this._allData[i].from===-2&&this._allOutputIndices.indexOf(i+t)===-1){t++,this._allData.splice(i,1),i--;continue}if(t>0){let u=-1;this._allData[i].from!==void 0&&this._allData[i].from!==-1?(u=this._nodes[this._allData[i].from].outputs.indexOf(i+t),u!==-1&&(this._nodes[this._allData[i].from].outputs[u]=i)):(u=this._allInputIndices.indexOf(i+t),u!==-1&&(this._allInputIndices[u]=i)),this._allData[i].to.forEach(n=>{u=this._nodes[n].inputs.indexOf(i+t),u!==-1&&(this._nodes[n].inputs[u]=i)}),this._allData[i].to.length===0&&(u=this._allOutputIndices.indexOf(i+t),u!==-1&&(this._allOutputIndices[u]=i))}}}deleteNode(t){let e=this._nodes[t];if(e.outputs.length>1){for(let s=1;s<e.outputs.length;s++)if(this._allData[e.outputs[s]].to.length>0)throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")}e.executeNode=!1;let o=e.inputs[0],i=e.outputs[0],u=this._allData[i].to;for(let s=0;s<e.inputs.length;s++){let d=this._allData[e.inputs[s]].to.indexOf(t);if(d===-1)throw new Error("The Value object doesn't have the current Node in it's 'to' property ");this._allData[e.inputs[s]].to.splice(d,1)}this._allData[i]._to=[];let n=this._allOutputIndices.indexOf(i);if(n!==-1&&(this._allOutputIndices[n]=o),u&&u.length>0)for(let s of u){let d=this._nodes[s].inputs.indexOf(i);if(d===-1)throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");this._nodes[s].inputs[d]=o,this._allData[o].to.push(s)}}removeAllDropoutNodes(){let t=0;for(let e of this._nodes){if(e.opType==="Dropout"){if(e.inputs.length!==1)throw new Error("Dropout nodes should only contain one input. 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Bundled license information:

long/index.js:
  (**
   * @license
   * Copyright 2009 The Closure Library Authors
   * Copyright 2020 Daniel Wirtz / The long.js Authors.
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   *
   * SPDX-License-Identifier: Apache-2.0
   *)
*/const ONNX_WEB=Object.freeze(Object.defineProperty({__proto__:null,get InferenceSession(){return Sd},get TRACE(){return cs},get TRACE_FUNC_BEGIN(){return ar},get TRACE_FUNC_END(){return sr},get Tensor(){return Re},get TrainingSession(){return Pd},default:zO,get env(){return Z},get registerBackend(){return or}},Symbol.toStringTag,{value:"Module"})),tensorToDataURL=(t,e)=>{const o=typeof document<"u"?document.createElement("canvas"):new OffscreenCanvas(1,1);o.width=t.dims[3],o.height=t.dims[2];const i=o.getContext("2d");if(i!=null){let u,n;e?.tensorLayout!==void 0&&e.tensorLayout==="NHWC"?(u=t.dims[2],n=t.dims[3]):(u=t.dims[3],n=t.dims[2]);const s=e?.format!==void 0?e.format:"RGB",d=e?.norm;let f,r;d===void 0||d.mean===void 0?f=[255,255,255,255]:typeof d.mean=="number"?f=[d.mean,d.mean,d.mean,d.mean]:(f=[d.mean[0],d.mean[1],d.mean[2],0],d.mean[3]!==void 0&&(f[3]=d.mean[3])),d===void 0||d.bias===void 0?r=[0,0,0,0]:typeof d.bias=="number"?r=[d.bias,d.bias,d.bias,d.bias]:(r=[d.bias[0],d.bias[1],d.bias[2],0],d.bias[3]!==void 0&&(r[3]=d.bias[3]));const a=n*u;let l=0,p=a,m=a*2,y=-1;s==="RGBA"?(l=0,p=a,m=a*2,y=a*3):s==="RGB"?(l=0,p=a,m=a*2):s==="RBG"&&(l=0,m=a,p=a*2);for(let T=0;T<n;T++)for(let v=0;v<u;v++){const S=(t.data[l++]-r[0])*f[0],E=(t.data[p++]-r[1])*f[1],A=(t.data[m++]-r[2])*f[2],F=y===-1?255:(t.data[y++]-r[3])*f[3];i.fillStyle="rgba("+S+","+E+","+A+","+F+")",i.fillRect(v,T,1,1)}if("toDataURL"in o)return o.toDataURL();throw new Error("toDataURL is not supported")}else throw new Error("Can not access image data")},tensorToImageData=(t,e)=>{const o=typeof document<"u"?document.createElement("canvas").getContext("2d"):new OffscreenCanvas(1,1).getContext("2d");let i;if(o!=null){let u,n,s;e?.tensorLayout!==void 0&&e.tensorLayout==="NHWC"?(u=t.dims[2],n=t.dims[1],s=t.dims[3]):(u=t.dims[3],n=t.dims[2],s=t.dims[1]);const d=e!==void 0&&e.format!==void 0?e.format:"RGB",f=e?.norm;let r,a;f===void 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must be defined");if(e.height===void 0||e.width===void 0)throw new Error("Image height and width must be defined");if(e.tensorLayout==="NHWC")throw new Error("NHWC Tensor layout is not supported yet");const{height:o,width:i}=e,u=e.norm??{mean:255,bias:0};let n,s;typeof u.mean=="number"?n=[u.mean,u.mean,u.mean,u.mean]:n=[u.mean[0],u.mean[1],u.mean[2],u.mean[3]??255],typeof u.bias=="number"?s=[u.bias,u.bias,u.bias,u.bias]:s=[u.bias[0],u.bias[1],u.bias[2],u.bias[3]??0];const d=e.format!==void 0?e.format:"RGBA",f=e.tensorFormat!==void 0&&e.tensorFormat!==void 0?e.tensorFormat:"RGB",r=o*i,a=f==="RGBA"?new Float32Array(r*4):new Float32Array(r*3);let l=4,p=0,m=1,y=2,T=3,v=0,S=r,E=r*2,A=-1;d==="RGB"&&(l=3,p=0,m=1,y=2,T=-1),f==="RGBA"?A=r*3:f==="RBG"?(v=0,E=r,S=r*2):f==="BGR"&&(E=0,S=r,v=r*2);for(let B=0;B<r;B++,p+=l,y+=l,m+=l,T+=l)a[v++]=(t[p]+s[0])/n[0],a[S++]=(t[m]+s[1])/n[1],a[E++]=(t[y]+s[2])/n[2],A!==-1&&T!==-1&&(a[A++]=(t[T]+s[3])/n[3]);return f==="RGBA"?new Tensor$2("float32",a,[1,4,o,i]):new Tensor$2("float32",a,[1,3,o,i])},tensorFromImage=async(t,e)=>{const o=typeof HTMLImageElement<"u"&&t instanceof HTMLImageElement,i=typeof ImageData<"u"&&t instanceof ImageData,u=typeof ImageBitmap<"u"&&t instanceof ImageBitmap,n=typeof t=="string";let s,d=e??{};const f=()=>{if(typeof document<"u")return document.createElement("canvas");if(typeof OffscreenCanvas<"u")return new OffscreenCanvas(1,1);throw new Error("Canvas is not supported")},r=a=>a instanceof HTMLCanvasElement||a instanceof OffscreenCanvas?a.getContext("2d"):null;if(o){const a=f();a.width=t.width,a.height=t.height;const l=r(a);if(l!=null){let p=t.height,m=t.width;if(e!==void 0&&e.resizedHeight!==void 0&&e.resizedWidth!==void 0&&(p=e.resizedHeight,m=e.resizedWidth),e!==void 0){if(d=e,e.tensorFormat!==void 0)throw new Error("Image input config format must be RGBA for HTMLImageElement");d.tensorFormat="RGBA",d.height=p,d.width=m}else d.tensorFormat="RGBA",d.height=p,d.width=m;l.drawImage(t,0,0),s=l.getImageData(0,0,m,p).data}else throw new Error("Can not access image data")}else if(i){let a,l;if(e!==void 0&&e.resizedWidth!==void 0&&e.resizedHeight!==void 0?(a=e.resizedHeight,l=e.resizedWidth):(a=t.height,l=t.width),e!==void 0&&(d=e),d.format="RGBA",d.height=a,d.width=l,e!==void 0){const p=f();p.width=l,p.height=a;const m=r(p);if(m!=null)m.putImageData(t,0,0),s=m.getImageData(0,0,l,a).data;else throw new Error("Can not access image data")}else s=t.data}else if(u){if(e===void 0)throw new Error("Please provide image config with format for Imagebitmap");const a=f();a.width=t.width,a.height=t.height;const l=r(a);if(l!=null){const p=t.height,m=t.width;return l.drawImage(t,0,0,m,p),s=l.getImageData(0,0,m,p).data,d.height=p,d.width=m,bufferToTensor(s,d)}else throw new Error("Can not access image data")}else{if(n)return new Promise((a,l)=>{const p=f(),m=r(p);if(!t||!m)return l();const y=new Image;y.crossOrigin="Anonymous",y.src=t,y.onload=()=>{p.width=y.width,p.height=y.height,m.drawImage(y,0,0,p.width,p.height);const T=m.getImageData(0,0,p.width,p.height);d.height=p.height,d.width=p.width,a(bufferToTensor(T.data,d))}});throw new Error("Input data provided is not supported - aborted tensor creation")}if(s!==void 0)return bufferToTensor(s,d);throw new Error("Input data provided is not supported - aborted tensor creation")},tensorFromTexture=(t,e)=>{const{width:o,height:i,download:u,dispose:n}=e,s=[1,i,o,4];return new Tensor$2({location:"texture",type:"float32",texture:t,dims:s,download:u,dispose:n})},tensorFromGpuBuffer=(t,e)=>{const{dataType:o,dims:i,download:u,dispose:n}=e;return new Tensor$2({location:"gpu-buffer",type:o??"float32",gpuBuffer:t,dims:i,download:u,dispose:n})},tensorFromPinnedBuffer=(t,e,o)=>new Tensor$2({location:"cpu-pinned",type:t,data:e,dims:o??[e.length]}),NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP=new Map([["float32",Float32Array],["uint8",Uint8Array],["int8",Int8Array],["uint16",Uint16Array],["float16",Uint16Array],["int16",Int16Array],["int32",Int32Array],["bool",Uint8Array],["float64",Float64Array],["uint32",Uint32Array]]),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP=new Map([[Float32Array,"float32"],[Uint8Array,"uint8"],[Int8Array,"int8"],[Uint16Array,"uint16"],[Int16Array,"int16"],[Int32Array,"int32"],[Float64Array,"float64"],[Uint32Array,"uint32"]]);let isBigIntChecked=!1;const checkBigInt=()=>{if(!isBigIntChecked){isBigIntChecked=!0;const t=typeof BigInt64Array<"u"&&typeof BigInt64Array.from=="function",e=typeof BigUint64Array<"u"&&typeof BigUint64Array.from=="function";t&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("int64",BigInt64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array,"int64")),e&&(NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("uint64",BigUint64Array),NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array,"uint64"))}},calculateSize=t=>{let e=1;for(let o=0;o<t.length;o++){const i=t[o];if(typeof i!="number"||!Number.isSafeInteger(i))throw new TypeError(`dims[${o}] must be an integer, got: ${i}`);if(i<0)throw new RangeError(`dims[${o}] must be a non-negative integer, got: ${i}`);e*=i}return e},tensorReshape=(t,e)=>{switch(t.location){case"cpu":return new Tensor$2(t.type,t.data,e);case"cpu-pinned":return new Tensor$2({location:"cpu-pinned",data:t.data,type:t.type,dims:e});case"texture":return new Tensor$2({location:"texture",texture:t.texture,type:t.type,dims:e});case"gpu-buffer":return new Tensor$2({location:"gpu-buffer",gpuBuffer:t.gpuBuffer,type:t.type,dims:e});default:throw new Error(`tensorReshape: tensor location ${t.location} is not supported`)}};let Tensor$2=class{constructor(e,o,i){checkBigInt();let u,n;if(typeof e=="object"&&"location"in e)switch(this.dataLocation=e.location,u=e.type,n=e.dims,e.location){case"cpu-pinned":{const d=NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(u);if(!d)throw new TypeError(`unsupported type "${u}" to 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Please use Uint16Array as data.");e==="uint64"||e==="int64"?d=r.from(o,BigInt):d=r.from(o)}else if(o instanceof r)d=o;else throw new TypeError(`A ${u} tensor's data must be type of ${r}`)}else if(f=o,Array.isArray(e)){if(e.length===0)throw new TypeError("Tensor type cannot be inferred from an empty array.");const r=typeof e[0];if(r==="string")u="string",d=e;else if(r==="boolean")u="bool",d=Uint8Array.from(e);else throw new TypeError(`Invalid element type of data array: ${r}.`)}else{const r=NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(e.constructor);if(r===void 0)throw new TypeError(`Unsupported type for tensor data: ${e.constructor}.`);u=r,d=e}if(f===void 0)f=[d.length];else if(!Array.isArray(f))throw new TypeError("A tensor's dims must be a number array");n=f,this.cpuData=d,this.dataLocation="cpu"}const s=calculateSize(n);if(this.cpuData&&s!==this.cpuData.length)throw new Error(`Tensor's size(${s}) does not match data length(${this.cpuData.length}).`);this.type=u,this.dims=n,this.size=s}static async fromImage(e,o){return tensorFromImage(e,o)}static fromTexture(e,o){return tensorFromTexture(e,o)}static fromGpuBuffer(e,o){return tensorFromGpuBuffer(e,o)}static fromPinnedBuffer(e,o,i){return tensorFromPinnedBuffer(e,o,i)}toDataURL(e){return tensorToDataURL(this,e)}toImageData(e){return tensorToImageData(this,e)}get data(){if(this.ensureValid(),!this.cpuData)throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");return this.cpuData}get location(){return this.dataLocation}get texture(){if(this.ensureValid(),!this.gpuTextureData)throw new Error("The data is not stored as a WebGL texture.");return this.gpuTextureData}get gpuBuffer(){if(this.ensureValid(),!this.gpuBufferData)throw new Error("The data is not stored as a WebGPU buffer.");return this.gpuBufferData}async getData(e){switch(this.ensureValid(),this.dataLocation){case"cpu":case"cpu-pinned":return this.data;case"texture":case"gpu-buffer":{if(!this.downloader)throw new Error("The current tensor is not created with a specified data downloader.");if(this.isDownloading)throw new Error("The current tensor is being downloaded.");try{this.isDownloading=!0;const o=await this.downloader();return this.downloader=void 0,this.dataLocation="cpu",this.cpuData=o,e&&this.disposer&&(this.disposer(),this.disposer=void 0),o}finally{this.isDownloading=!1}}default:throw new Error(`cannot get data from location: ${this.dataLocation}`)}}dispose(){if(this.isDownloading)throw new Error("The current tensor is being downloaded.");this.disposer&&(this.disposer(),this.disposer=void 0),this.cpuData=void 0,this.gpuTextureData=void 0,this.gpuBufferData=void 0,this.downloader=void 0,this.isDownloading=void 0,this.dataLocation="none"}ensureValid(){if(this.dataLocation==="none")throw new Error("The tensor is disposed.")}reshape(e){if(this.ensureValid(),this.downloader||this.disposer)throw new Error("Cannot reshape a tensor that owns GPU resource.");return tensorReshape(this,e)}};const Tensor$1=Tensor$2;let ONNX;const WEBGPU_AVAILABLE=typeof navigator<"u"&&"gpu"in navigator,USE_ONNXRUNTIME_NODE=typeof process<"u"&&process?.release?.name==="node",ONNX_MODULES=new Map;USE_ONNXRUNTIME_NODE?(ONNX=sharp??ONNX_NODE,ONNX_MODULES.set("node",ONNX)):(ONNX=zO??ONNX_WEB,ONNX_MODULES.set("web",ONNX));async function createInferenceSession(t,e){let o,i;if(USE_ONNXRUNTIME_NODE){const s=ONNX_MODULES.get("node");i=s.InferenceSession,o=["cpu"],Object.assign(s.env,env.backends.onnx)}else if(WEBGPU_AVAILABLE&&env.experimental.useWebGPU){let s=ONNX_MODULES.get("webgpu");s===void 0&&(s=await __vitePreload(()=>import("./ort.webgpu.min-7V0I_OOb.js"),__vite__mapDeps([])),ONNX_MODULES.set("webgpu",s)),i=s.InferenceSession,o=["webgpu","wasm"],Object.assign(s.env,env.backends.onnx)}else{const s=ONNX_MODULES.get("web");i=s.InferenceSession,o=["wasm"],Object.assign(s.env,env.backends.onnx)}const u={executionProviders:o,...e};return await i.create(t,u)}function isONNXTensor(t){for(const e of ONNX_MODULES.values())if(t instanceof e.Tensor)return!0;return!1}const ONNX_ENV=ONNX?.env;ONNX_ENV&&ONNX_ENV.wasm&&(ONNX_ENV.wasm.wasmPaths=RUNNING_LOCALLY?sharp.join(env.__dirname,"/dist/"):`https://cdn.jsdelivr.net/npm/@xenova/transformers@${env.version}/dist/`,typeof navigator<"u"&&/iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent)&&(ONNX_ENV.wasm.simd=!1),ONNX_ENV.wasm.proxy=!0);function isONNXProxy(){return ONNX_ENV.wasm.proxy}env.backends.onnx=ONNX_ENV;const DataTypeMap=Object.freeze({float32:Float32Array,float16:Uint16Array,float64:Float64Array,string:Array,int8:Int8Array,uint8:Uint8Array,int16:Int16Array,uint16:Uint16Array,int32:Int32Array,uint32:Uint32Array,int64:BigInt64Array,uint64:BigUint64Array,bool:Uint8Array});class Tensor{get dims(){return this.ort_tensor.dims}set dims(e){this.ort_tensor.dims=e}get type(){return this.ort_tensor.type}get data(){return this.ort_tensor.data}get size(){return this.ort_tensor.size}ort_tensor;constructor(...e){return isONNXTensor(e[0])?this.ort_tensor=e[0]:this.ort_tensor=new Tensor$1(e[0],e[1],e[2]),new Proxy(this,{get:(o,i)=>{if(typeof i=="string"){let u=Number(i);if(Number.isInteger(u))return o._getitem(u)}return o[i]},set:(o,i,u)=>o[i]=u})}dispose(){this.ort_tensor.dispose()}*[Symbol.iterator](){const[e,...o]=this.dims;if(o.length>0){const i=o.reduce((u,n)=>u*n);for(let u=0;u<e;++u)yield this._subarray(u,i,o)}else yield*this.data}_getitem(e){const[o,...i]=this.dims;if(e=safeIndex(e,o),i.length>0){const u=i.reduce((n,s)=>n*s);return this._subarray(e,u,i)}else return new Tensor(this.type,[this.data[e]],i)}indexOf(e){for(let o=0;o<this.data.length;++o)if(this.data[o]==e)return o;return-1}_subarray(e,o,i){const u=e*o,n=(e+1)*o,s="subarray"in this.data?this.data.subarray(u,n):this.data.slice(u,n);return new Tensor(this.type,s,i)}item(){if(this.data.length!==1)throw new Error(`a Tensor with ${this.data.length} elements cannot be converted to Scalar`);return this.data[0]}tolist(){return reshape(this.data,this.dims)}sigmoid(){return this.clone().sigmoid_()}sigmoid_(){for(let e=0;e<this.data.length;++e)this.data[e]=1/(1+Math.exp(-this.data[e]));return this}mul(e){return this.clone().mul_(e)}mul_(e){for(let o=0;o<this.data.length;++o)this.data[o]*=e;return this}add(e){return this.clone().add_(e)}add_(e){for(let o=0;o<this.data.length;++o)this.data[o]+=e;return this}clone(){return new Tensor(this.type,this.data.slice(),this.dims.slice())}slice(...e){let o=[],i=[];for(let f=0;f<this.dims.length;++f){let r=e[f];if(r==null)i.push([0,this.dims[f]]),o.push(this.dims[f]);else if(typeof r=="number")r=safeIndex(r,this.dims[f],f),i.push([r,r+1]);else if(Array.isArray(r)&&r.length===2){if(r[0]>r[1])throw new Error(`Invalid slice: ${r}`);let a=[Math.max(r[0],0),Math.min(r[1],this.dims[f])];i.push(a),o.push(a[1]-a[0])}else throw new Error(`Invalid slice: ${r}`)}let u=i.map(([f,r])=>r-f),n=u.reduce((f,r)=>f*r),s=new this.data.constructor(n);const d=this.stride();for(let f=0;f<n;++f){let r=0;for(let a=u.length-1,l=f;a>=0;--a){const p=u[a];r+=(l%p+i[a][0])*d[a],l=Math.floor(l/p)}s[f]=this.data[r]}return new Tensor(this.type,s,o)}transpose(...e){return transpose(this,e)}sum(e=null,o=!1){return this.norm(1,e,o)}norm(e="fro",o=null,i=!1){if(e==="fro")e=2;else if(typeof e=="string")throw Error(`Unsupported norm: ${e}`);if(o===null){let s=this.data.reduce((d,f)=>d+f**e,0)**(1/e);return new Tensor(this.type,[s],[])}o=safeIndex(o,this.dims.length);const u=this.dims.slice();u[o]=1;const n=new this.data.constructor(this.data.length/this.dims[o]);for(let s=0;s<this.data.length;++s){let d=0;for(let f=this.dims.length-1,r=s,a=1;f>=0;--f){const l=this.dims[f];if(f!==o){const p=r%l;d+=p*a,a*=u[f]}r=Math.floor(r/l)}n[d]+=this.data[s]**e}if(e!==1)for(let s=0;s<n.length;++s)n[s]=n[s]**(1/e);return i||u.splice(o,1),new Tensor(this.type,n,u)}normalize_(e=2,o=1){o=safeIndex(o,this.dims.length);const i=this.norm(e,o,!0);for(let u=0;u<this.data.length;++u){let n=0;for(let s=this.dims.length-1,d=u,f=1;s>=0;--s){const r=this.dims[s];if(s!==o){const a=d%r;n+=a*f,f*=this.dims[s]}d=Math.floor(d/r)}this.data[u]/=i.data[n]}return this}normalize(e=2,o=1){return this.clone().normalize_(e,o)}stride(){return dimsToStride(this.dims)}squeeze(e=null){return new Tensor(this.type,this.data,calc_squeeze_dims(this.dims,e))}squeeze_(e=null){return this.dims=calc_squeeze_dims(this.dims,e),this}unsqueeze(e=null){return new Tensor(this.type,this.data,calc_unsqueeze_dims(this.dims,e))}unsqueeze_(e=null){return this.dims=calc_unsqueeze_dims(this.dims,e),this}flatten_(e=0,o=-1){o=(o+this.dims.length)%this.dims.length;let i=this.dims.slice(0,e),u=this.dims.slice(e,o+1),n=this.dims.slice(o+1);return this.dims=[...i,u.reduce((s,d)=>s*d,1),...n],this}flatten(e=0,o=-1){return this.clone().flatten_(e,o)}view(...e){let o=-1;for(let i=0;i<e.length;++i)if(e[i]===-1){if(o!==-1)throw new Error("Only one dimension can be inferred");o=i}if(o!==-1){const i=e.reduce((u,n,s)=>s!==o?u*n:u,1);e[o]=this.data.length/i}return new Tensor(this.type,this.data,e)}neg_(){for(let e=0;e<this.data.length;++e)this.data[e]=-this.data[e];return this}neg(){return this.clone().neg_()}clamp_(e,o){for(let i=0;i<this.data.length;++i)this.data[i]=Math.min(Math.max(this.data[i],e),o);return this}clamp(e,o){return this.clone().clamp_(e,o)}round_(){for(let e=0;e<this.data.length;++e)this.data[e]=Math.round(this.data[e]);return this}round(){return this.clone().round_()}to(e){if(this.type===e)return this;if(!DataTypeMap.hasOwnProperty(e))throw new Error(`Unsupported type: ${e}`);return new Tensor(e,DataTypeMap[e].from(this.data),this.dims)}}function reshape(t,e){const o=t.length,i=e.reduce((n,s)=>n*s);if(o!==i)throw Error(`cannot reshape array of size ${o} into shape (${e})`);let u=t;for(let n=e.length-1;n>=0;n--)u=u.reduce((s,d)=>{let f=s[s.length-1];return f.length<e[n]?f.push(d):s.push([d]),s},[[]]);return u[0]}function transpose(t,e){const[o,i]=transpose_data(t.data,t.dims,e);return new Tensor(t.type,o,i)}function interpolate(t,[e,o],i="bilinear",u=!1){const n=t.dims.at(-3)??1,s=t.dims.at(-2),d=t.dims.at(-1);let f=interpolate_data(t.data,[n,s,d],[e,o],i,u);return new Tensor(t.type,f,[n,e,o])}function calc_squeeze_dims(t,e){return t=t.slice(),e===null?t=t.filter(o=>o!==1):typeof e=="number"?t[e]===1&&t.splice(e,1):Array.isArray(e)&&(t=t.filter((o,i)=>o!==1||!e.includes(i))),t}function calc_unsqueeze_dims(t,e){return e=safeIndex(e,t.length+1),t=t.slice(),t.splice(e,0,1),t}function safeIndex(t,e,o=null){if(t<-e||t>=e)throw new Error(`IndexError: index ${t} is out of bounds for dimension${o===null?"":" "+o} with size ${e}`);return t<0&&(t=(t%e+e)%e),t}function cat(t,e=0){e=safeIndex(e,t[0].dims.length);const o=t[0].dims.slice();o[e]=t.reduce((s,d)=>s+d.dims[e],0);const i=o.reduce((s,d)=>s*d,1),u=new t[0].data.constructor(i),n=t[0].type;if(e===0){let s=0;for(let d of t)u.set(d.data,s),s+=d.data.length}else{let s=0;for(let d=0;d<t.length;++d){let f=t[d];for(let r=0;r<f.data.length;++r){let a=0;for(let l=f.dims.length-1,p=r,m=1;l>=0;--l){const y=f.dims[l];let T=p%y;l===e&&(T+=s),a+=T*m,m*=o[l],p=Math.floor(p/y)}u[a]=f.data[r]}s+=f.dims[e]}}return new Tensor(n,u,o)}function stack(t,e=0){return cat(t.map(o=>o.unsqueeze(e)),e)}function std_mean(t,e=null,o=1,i=!1){if(e===null){const r=t.data.reduce((m,y)=>m+y,0)/t.data.length,a=Math.sqrt(t.data.reduce((m,y)=>m+(y-r)**2,0)/(t.data.length-o)),l=new Tensor(t.type,[r],[]);return[new Tensor(t.type,[a],[]),l]}e=safeIndex(e,t.dims.length);const u=mean(t,e,i),n=t.dims.slice();n[e]=1;const s=new t.data.constructor(t.data.length/t.dims[e]);for(let f=0;f<t.data.length;++f){let r=0;for(let a=t.dims.length-1,l=f,p=1;a>=0;--a){const m=t.dims[a];if(a!==e){const y=l%m;r+=y*p,p*=n[a]}l=Math.floor(l/m)}s[r]+=(t.data[f]-u.data[r])**2}for(let f=0;f<s.length;++f)s[f]=Math.sqrt(s[f]/(t.dims[e]-o));return i||n.splice(e,1),[new Tensor(t.type,s,n),u]}function mean(t,e=null,o=!1){if(e===null){let n=t.data.reduce((s,d)=>s+d,0);return new Tensor(t.type,[n/t.data.length],[])}e=safeIndex(e,t.dims.length);const i=t.dims.slice();i[e]=1;const u=new t.data.constructor(t.data.length/t.dims[e]);for(let n=0;n<t.data.length;++n){let s=0;for(let d=t.dims.length-1,f=n,r=1;d>=0;--d){const a=t.dims[d];if(d!==e){const l=f%a;s+=l*r,r*=i[d]}f=Math.floor(f/a)}u[s]+=t.data[n]}if(t.dims[e]!==1)for(let n=0;n<u.length;++n)u[n]=u[n]/t.dims[e];return o||i.splice(e,1),new Tensor(t.type,u,i)}function dynamicTimeWarping(t){const[e,o]=t.dims,i=[e+1,o+1],u=new Tensor("float32",new Float32Array(i[0]*i[1]).fill(1/0),i),n=new Tensor("float32",new Float32Array(i[0]*i[1]).fill(-1),i);u[0].data[0]=0;for(let a=1;a<o+1;++a)for(let l=1;l<e+1;++l){const p=u[l-1][a-1].item(),m=u[l-1][a].item(),y=u[l][a-1].item();let T,v;p<m&&p<y?(T=p,v=0):m<p&&m<y?(T=m,v=1):(T=y,v=2),u[l].data[a]=t[l-1][a-1].item()+T,n[l].data[a]=v}let s=e,d=o;n.data.fill(2,0,i[1]);for(let a=0;a<i[0];++a)n[a].data[0]=1;let f=[],r=[];for(;s>0||d>0;)switch(f.push(s-1),r.push(d-1),n[s][d].item()){case 0:--s,--d;break;case 1:--s;break;case 2:--d;break;default:throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${d}]. Please file a bug report.`)}return f.reverse(),r.reverse(),[f,r]}function dimsToStride(t){const e=new Array(t.length);for(let o=t.length-1,i=1;o>=0;--o)e[o]=i,i*=t[o];return e}function ones(t){const e=t.reduce((o,i)=>o*i,1);return new Tensor("int64",new BigInt64Array(e).fill(1n),t)}function ones_like(t){return ones(t.dims)}var TOKEN_TYPES=Object.freeze({Text:"Text",NumericLiteral:"NumericLiteral",BooleanLiteral:"BooleanLiteral",StringLiteral:"StringLiteral",Identifier:"Identifier",Equals:"Equals",OpenParen:"OpenParen",CloseParen:"CloseParen",OpenStatement:"OpenStatement",CloseStatement:"CloseStatement",OpenExpression:"OpenExpression",CloseExpression:"CloseExpression",OpenSquareBracket:"OpenSquareBracket",CloseSquareBracket:"CloseSquareBracket",OpenCurlyBracket:"OpenCurlyBracket",CloseCurlyBracket:"CloseCurlyBracket",Comma:"Comma",Dot:"Dot",Colon:"Colon",Pipe:"Pipe",CallOperator:"CallOperator",AdditiveBinaryOperator:"AdditiveBinaryOperator",MultiplicativeBinaryOperator:"MultiplicativeBinaryOperator",ComparisonBinaryOperator:"ComparisonBinaryOperator",UnaryOperator:"UnaryOperator",Set:"Set",If:"If",For:"For",In:"In",Is:"Is",NotIn:"NotIn",Else:"Else",EndIf:"EndIf",ElseIf:"ElseIf",EndFor:"EndFor",And:"And",Or:"Or",Not:"UnaryOperator"});Object.freeze({set:TOKEN_TYPES.Set,for:TOKEN_TYPES.For,in:TOKEN_TYPES.In,is:TOKEN_TYPES.Is,if:TOKEN_TYPES.If,else:TOKEN_TYPES.Else,endif:TOKEN_TYPES.EndIf,elif:TOKEN_TYPES.ElseIf,endfor:TOKEN_TYPES.EndFor,and:TOKEN_TYPES.And,or:TOKEN_TYPES.Or,not:TOKEN_TYPES.Not,"not in":TOKEN_TYPES.NotIn,true:TOKEN_TYPES.BooleanLiteral,false:TOKEN_TYPES.BooleanLiteral});TOKEN_TYPES.OpenStatement,TOKEN_TYPES.CloseStatement,TOKEN_TYPES.OpenExpression,TOKEN_TYPES.CloseExpression,TOKEN_TYPES.OpenParen,TOKEN_TYPES.CloseParen,TOKEN_TYPES.OpenCurlyBracket,TOKEN_TYPES.CloseCurlyBracket,TOKEN_TYPES.OpenSquareBracket,TOKEN_TYPES.CloseSquareBracket,TOKEN_TYPES.Comma,TOKEN_TYPES.Dot,TOKEN_TYPES.Colon,TOKEN_TYPES.Pipe,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.ComparisonBinaryOperator,TOKEN_TYPES.AdditiveBinaryOperator,TOKEN_TYPES.AdditiveBinaryOperator,TOKEN_TYPES.MultiplicativeBinaryOperator,TOKEN_TYPES.MultiplicativeBinaryOperator,TOKEN_TYPES.MultiplicativeBinaryOperator,TOKEN_TYPES.Equals;const BYTES_TO_UNICODE=(()=>{const t=[...Array.from({length:94},(u,n)=>n+33),...Array.from({length:12},(u,n)=>n+161),...Array.from({length:82},(u,n)=>n+174)],e=t.slice();let o=0;for(let u=0;u<256;++u)t.includes(u)||(t.push(u),e.push(256+o),o+=1);const i=e.map(u=>String.fromCharCode(u));return Object.fromEntries(t.map((u,n)=>[u,i[n]]))})();reverseDictionary(BYTES_TO_UNICODE);const WHISPER_LANGUAGES=[["en","english"],["zh","chinese"],["de","german"],["es","spanish"],["ru","russian"],["ko","korean"],["fr","french"],["ja","japanese"],["pt","portuguese"],["tr","turkish"],["pl","polish"],["ca","catalan"],["nl","dutch"],["ar","arabic"],["sv","swedish"],["it","italian"],["id","indonesian"],["hi","hindi"],["fi","finnish"],["vi","vietnamese"],["he","hebrew"],["uk","ukrainian"],["el","greek"],["ms","malay"],["cs","czech"],["ro","romanian"],["da","danish"],["hu","hungarian"],["ta","tamil"],["no","norwegian"],["th","thai"],["ur","urdu"],["hr","croatian"],["bg","bulgarian"],["lt","lithuanian"],["la","latin"],["mi","maori"],["ml","malayalam"],["cy","welsh"],["sk","slovak"],["te","telugu"],["fa","persian"],["lv","latvian"],["bn","bengali"],["sr","serbian"],["az","azerbaijani"],["sl","slovenian"],["kn","kannada"],["et","estonian"],["mk","macedonian"],["br","breton"],["eu","basque"],["is","icelandic"],["hy","armenian"],["ne","nepali"],["mn","mongolian"],["bs","bosnian"],["kk","kazakh"],["sq","albanian"],["sw","swahili"],["gl","galician"],["mr","marathi"],["pa","punjabi"],["si","sinhala"],["km","khmer"],["sn","shona"],["yo","yoruba"],["so","somali"],["af","afrikaans"],["oc","occitan"],["ka","georgian"],["be","belarusian"],["tg","tajik"],["sd","sindhi"],["gu","gujarati"],["am","amharic"],["yi","yiddish"],["lo","lao"],["uz","uzbek"],["fo","faroese"],["ht","haitian creole"],["ps","pashto"],["tk","turkmen"],["nn","nynorsk"],["mt","maltese"],["sa","sanskrit"],["lb","luxembourgish"],["my","myanmar"],["bo","tibetan"],["tl","tagalog"],["mg","malagasy"],["as","assamese"],["tt","tatar"],["haw","hawaiian"],["ln","lingala"],["ha","hausa"],["ba","bashkir"],["jw","javanese"],["su","sundanese"]];new Map(WHISPER_LANGUAGES);async function loadConfig(t,e){return await getModelJSON(t,"config.json",!0,e)}class PretrainedConfig{constructor(e){this.model_type=null,this.is_encoder_decoder=!1,Object.assign(this,e)}static async from_pretrained(e,{progress_callback:o=null,config:i=null,cache_dir:u=null,local_files_only:n=!1,revision:s="main"}={}){let d=i??await loadConfig(e,{progress_callback:o,config:i,cache_dir:u,local_files_only:n,revision:s});return new this(d)}}class AutoConfig{static async from_pretrained(...e){return PretrainedConfig.from_pretrained(...e)}}class LogitsProcessorList extends Callable{constructor(){super(),this.processors=[]}push(e){this.processors.push(e)}extend(e){this.processors.push(...e)}_call(e,o){for(let i of o)this.processors.forEach(u=>u(e,i))}[Symbol.iterator](){return this.processors.values()}}class LogitsProcessor extends Callable{_call(e,o){throw Error("`_call` should be implemented in a subclass")}}class ForceTokensLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.force_token_map=Object.fromEntries(e??[])}_call(e,o){let i=this.force_token_map[e.length];return exists(i)&&(o.data.fill(-1/0),o.data[i]=0),o}}class ForcedBOSTokenLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.bos_token_id=e}_call(e,o){return e.length===1&&(o.data.fill(-1/0),o.data[this.bos_token_id]=0),o}}class ForcedEOSTokenLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.max_length=e,this.forced_eos_token_id=o}_call(e,o){}}class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.begin_suppress_tokens=e,this.begin_index=o}_call(e,o){if(e.length===this.begin_index)for(let i of this.begin_suppress_tokens)o.data[i]=-1/0;return o}}class WhisperTimeStampLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.eos_token_id=e.eos_token_id,this.no_timestamps_token_id=e.no_timestamps_token_id,this.timestamp_begin=this.no_timestamps_token_id+1,this.begin_index=(e.forced_decoder_ids||[]).length+2,e.forced_decoder_ids.slice(-1)[0][1]===this.no_timestamps_token_id&&(this.begin_index-=1),this.max_initial_timestamp_index=e.max_initial_timestamp_index}_call(e,o){const i=o.data;if(i[this.no_timestamps_token_id]=-1/0,e.length===this.begin_index-1)return i.fill(-1/0),i[this.timestamp_begin]=0,o;const u=e.slice(this.begin_index),n=u.length>=1&&u[u.length-1]>=this.timestamp_begin,s=u.length<2||u[u.length-2]>=this.timestamp_begin;if(n&&(s?i.subarray(this.timestamp_begin).fill(-1/0):i.subarray(0,this.eos_token_id).fill(-1/0)),e.length===this.begin_index&&this.max_initial_timestamp_index!==null){const a=this.timestamp_begin+this.max_initial_timestamp_index;i.subarray(a+1).fill(-1/0)}const d=log_softmax(i),f=Math.log(d.subarray(this.timestamp_begin).map(Math.exp).reduce((a,l)=>a+l)),r=max(d.subarray(0,this.timestamp_begin))[0];return f>r&&i.subarray(0,this.timestamp_begin).fill(-1/0),o}}class NoRepeatNGramLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.no_repeat_ngram_size=e}getNgrams(e){const o=e.length,i=[];for(let n=0;n<o+1-this.no_repeat_ngram_size;++n){const s=[];for(let d=0;d<this.no_repeat_ngram_size;++d)s.push(e[n+d]);i.push(s)}const u=new Map;for(const n of i){const s=n.slice(0,n.length-1),d=JSON.stringify(s),f=u.get(d)??[];f.push(n[n.length-1]),u.set(d,f)}return u}getGeneratedNgrams(e,o){const i=o.slice(o.length+1-this.no_repeat_ngram_size,o.length);return e.get(JSON.stringify(i))??[]}calcBannedNgramTokens(e){const o=[];if(e.length+1<this.no_repeat_ngram_size)return o;{const i=this.getNgrams(e);return this.getGeneratedNgrams(i,e)}}_call(e,o){const i=this.calcBannedNgramTokens(e);for(const u of i)o.data[u]=-1/0;return o}}class RepetitionPenaltyLogitsProcessor extends LogitsProcessor{constructor(e){super(),this.penalty=e}_call(e,o){for(const i of e)o.data[i]<0?o.data[i]*=this.penalty:o.data[i]/=this.penalty;return o}}class MinLengthLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.min_length=e,this.eos_token_id=Array.isArray(o)?o:[o]}_call(e,o){if(e.length<this.min_length)for(const i of this.eos_token_id)o.data[i]=-1/0;return o}}class MinNewTokensLengthLogitsProcessor extends LogitsProcessor{constructor(e,o,i){super(),this.prompt_length_to_skip=e,this.min_new_tokens=o,this.eos_token_id=Array.isArray(i)?i:[i]}_call(e,o){if(e.length-this.prompt_length_to_skip<this.min_new_tokens)for(const u of this.eos_token_id)o.data[u]=-1/0;return o}}class NoBadWordsLogitsProcessor extends LogitsProcessor{constructor(e,o){super(),this.bad_words_ids=e,this.eos_token_id=Array.isArray(o)?o:[o]}_call(e,o){for(const i of this.bad_words_ids){let u=!0;for(let n=1;n<=i.length-1&&i.length<e.length;++n)if(i.at(-n-1)!==e.at(-n)){u=!1;break}u&&(o.data[i.at(-1)]=-1/0)}return o}}const GenerationConfig=class{constructor(t={}){this.max_length=t.max_length??20,this.max_new_tokens=t.max_new_tokens??null,this.min_length=t.min_length??0,this.min_new_tokens=t.min_new_tokens??null,this.early_stopping=t.early_stopping??!1,this.max_time=t.max_time??null,this.do_sample=t.do_sample??!1,this.num_beams=t.num_beams??1,this.num_beam_groups=t.num_beam_groups??1,this.penalty_alpha=t.penalty_alpha??null,this.use_cache=t.use_cache??!0,this.temperature=t.temperature??1,this.top_k=t.top_k??50,this.top_p=t.top_p??1,this.typical_p=t.typical_p??1,this.epsilon_cutoff=t.epsilon_cutoff??0,this.eta_cutoff=t.eta_cutoff??0,this.diversity_penalty=t.diversity_penalty??0,this.repetition_penalty=t.repetition_penalty??1,this.encoder_repetition_penalty=t.encoder_repetition_penalty??1,this.length_penalty=t.length_penalty??1,this.no_repeat_ngram_size=t.no_repeat_ngram_size??0,this.bad_words_ids=t.bad_words_ids??null,this.force_words_ids=t.force_words_ids??null,this.renormalize_logits=t.renormalize_logits??!1,this.constraints=t.constraints??null,this.forced_bos_token_id=t.forced_bos_token_id??null,this.forced_eos_token_id=t.forced_eos_token_id??null,this.remove_invalid_values=t.remove_invalid_values??!1,this.exponential_decay_length_penalty=t.exponential_decay_length_penalty??null,this.suppress_tokens=t.suppress_tokens??null,this.begin_suppress_tokens=t.begin_suppress_tokens??null,this.forced_decoder_ids=t.forced_decoder_ids??null,this.num_return_sequences=t.num_return_sequences??1,this.output_attentions=t.output_attentions??!1,this.output_hidden_states=t.output_hidden_states??!1,this.output_scores=t.output_scores??!1,this.return_dict_in_generate=t.return_dict_in_generate??!1,this.pad_token_id=t.pad_token_id??null,this.bos_token_id=t.bos_token_id??null,this.eos_token_id=t.eos_token_id??null,this.encoder_no_repeat_ngram_size=t.encoder_no_repeat_ngram_size??0,this.decoder_start_token_id=t.decoder_start_token_id??null,this.generation_kwargs=t.generation_kwargs??{}}};class Sampler extends Callable{constructor(e){super(),this.generation_config=e}_call(e,o=-1){return this.sample(e,o)}sample(e,o){throw Error("sample should be implemented in subclasses.")}getLogits(e,o){let i=e.dims.at(-1),u=e.data;if(o===-1)u=u.slice(-i);else{let n=o*i;u=u.slice(n,n+i)}return this.generation_config.temperature>0&&(u=u.map(n=>n/this.generation_config.temperature)),u}randomSelect(e){let o=e.reduce((u,n)=>u+n,0),i=Math.random()*o;for(let u=0;u<e.length;++u)if(i-=e[u],i<=0)return u;return 0}static getSampler(e){if(e.do_sample)return new MultinomialSampler(e);if(e.num_beams>1)return new BeamSearchSampler(e);if(e.num_return_sequences>1)throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${e.num_return_sequences}.`);return new GreedySampler(e)}}class GreedySampler extends Sampler{sample(e,o=-1){let i=this.getLogits(e,o);return[[max(i)[1],0]]}}class MultinomialSampler extends Sampler{sample(e,o=-1){let i=e.dims.at(-1);this.generation_config.top_k>0&&(i=Math.min(this.generation_config.top_k,i));const u=this.getLogits(e,o),n=getTopItems(u,i),s=softmax(n.map(d=>d[1]));return Array.from({length:this.generation_config.num_beams},()=>{const d=this.randomSelect(s);return[n[d][0],Math.log(s[d])]})}}class BeamSearchSampler extends Sampler{sample(e,o=-1){let i=e.dims.at(-1);this.generation_config.top_k>0&&(i=Math.min(this.generation_config.top_k,i));const u=this.getLogits(e,o),n=getTopItems(u,i),s=softmax(n.map(d=>d[1]));return Array.from({length:this.generation_config.num_beams},(d,f)=>[n[f][0],Math.log(s[f])])}}const MODEL_TYPES={EncoderOnly:0,EncoderDecoder:1,Seq2Seq:2,Vision2Seq:3,DecoderOnly:4,MaskGeneration:5},MODEL_TYPE_MAPPING=new Map,MODEL_NAME_TO_CLASS_MAPPING=new Map,MODEL_CLASS_TO_NAME_MAPPING=new Map;async function constructSession(t,e,o){const i=`onnx/${e}${o.quantized?"_quantized":""}.onnx`,u=await getModelFile(t,i,!0,o),n=o.session_options??{};if(n.externalData!==void 0)for(let s=0;s<n.externalData.length;s++){const d=n.externalData[s];if(typeof d.data=="string"){const f=await getModelFile(t,d.data,!0,o);d.data=f}}return await createInferenceSession(u,n)}function validateInputs(t,e){const o=Object.create(null),i=[];for(const s of t.inputNames){const d=e[s];if(!(d instanceof Tensor)){i.push(s);continue}o[s]=isONNXProxy()?d.clone():d}if(i.length>0)throw new Error(`An error occurred during model execution: "Missing the following inputs: ${i.join(", ")}.`);const u=Object.keys(e).length,n=t.inputNames.length;if(u>n){let s=Object.keys(e).filter(d=>!t.inputNames.includes(d));console.warn(`WARNING: Too many inputs were provided (${u} > ${n}). The following inputs will be ignored: "${s.join(", ")}".`)}return o}async function sessionRun(t,e){const o=validateInputs(t,e);try{const i=Object.fromEntries(Object.entries(o).map(([n,s])=>[n,s.ort_tensor]));let u=await t.run(i);u=replaceTensors(u);for(const[n,s]of Object.entries(o))n.startsWith("past_key_values")&&s.dispose();return u}catch(i){throw console.error(`An error occurred during model execution: "${i}".`),console.error("Inputs given to model:",o),i}}function replaceTensors(t){for(let e in t)isONNXTensor(t[e])?t[e]=new Tensor(t[e]):typeof t[e]=="object"&&replaceTensors(t[e]);return t}function toI64Tensor(t){if(t instanceof Tensor)return t;if(t.length===0)throw Error("items must be non-empty");if(Array.isArray(t[0])){if(t.some(e=>e.length!==t[0].length))throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");return new Tensor("int64",BigInt64Array.from(t.flat().map(e=>BigInt(e))),[t.length,t[0].length])}else return new Tensor("int64",BigInt64Array.from(t.map(e=>BigInt(e))),[1,t.length])}function prepareAttentionMask(t,e){let o=t.config.pad_token_id??null,i=t.config.eos_token_id??null;isIntegralNumber(i)&&(i=[i]);let u=e.indexOf(o)!==-1,n=i===null||!i.includes(o);if(u&&n){let s=BigInt64Array.from(e.data.map(d=>d!=o));return new Tensor("int64",s,e.dims)}else return ones_like(e)}function preparePositionIds(t,e,o){if(!t.inputNames.includes("position_ids"))return;const i=new BigInt64Array(e.attention_mask.data.length);for(let u=0;u<e.attention_mask.dims[0];++u){let n=u*e.attention_mask.dims[1],s=BigInt(0);for(let d=0;d<e.attention_mask.dims[1];++d){const f=n+d;e.attention_mask.data[f]===0n?i[f]=BigInt(1):(i[f]=s,s+=e.attention_mask.data[f])}}e.position_ids=new Tensor("int64",i,e.attention_mask.dims),o&&(e.position_ids=e.position_ids.slice(null,-1).unsqueeze_(-1))}function boolTensor(t){return new Tensor("bool",[t],[1])}async function seq2seqForward(t,e){let{encoder_outputs:o,past_key_values:i}=e;o||(o=(await encoderForward(t,e)).last_hidden_state);let u={input_ids:e.decoder_input_ids,encoder_hidden_states:o};const n=!!i;t.decoder_merged_session.inputNames.includes("use_cache_branch")&&(u.use_cache_branch=boolTensor(n)),t.decoder_merged_session.inputNames.includes("encoder_attention_mask")&&(u.encoder_attention_mask=e.attention_mask),preparePositionIds(t.decoder_merged_session,u,n),t.addPastKeyValues(u,i);const s=await sessionRun(t.decoder_merged_session,u);let d=s.logits;i=t.getPastKeyValues(s,i);const f=t.getAttentions(s);return new Seq2SeqLMOutput({logits:d,past_key_values:i,encoder_outputs:o,...f})}function seq2seqStartBeams(t,e,o,i){let u=[],n=0;const s=t.requires_attention_mask??!0;let d=o.decoder_input_ids??o.decoder_start_token_id??o.bos_token_id??o.eos_token_id;d instanceof Tensor?d=d.tolist().flat():Array.isArray(d)||(d=[d]);for(let f of e){f.dims=[1,...f.dims];let r={inputs:f,encoder_outputs:null,prev_model_outputs:null,output_token_ids:d,done:!1,score:0,id:n++};s&&(r.attention_mask=prepareAttentionMask(t,f)),u.push(r)}return u}async function seq2seqRunBeam(t,e){const o=t.main_input_name;let i=e.output_token_ids;e.prev_model_outputs&&(i=i.slice(-1));let u={[o]:e.inputs,decoder_input_ids:toI64Tensor(i),encoder_outputs:e.encoder_outputs,past_key_values:e.prev_model_outputs?.past_key_values};e.attention_mask&&(u.attention_mask=e.attention_mask);let n=await t.forward(u);return e.prev_model_outputs=n,e.encoder_outputs=n.encoder_outputs,n}function seq2seqUpdatebeam(t,e){t.output_token_ids=[...t.output_token_ids,e]}async function encoderForward(t,e){const o=Object.create(null);for(const i of t.session.inputNames)o[i]=e[i];return t.session.inputNames.includes("token_type_ids")&&!o.token_type_ids&&(o.token_type_ids=new Tensor("int64",new BigInt64Array(o.input_ids.data.length),o.input_ids.dims)),await sessionRun(t.session,o)}async function decoderForward(t,e){let{input_ids:o,past_key_values:i,attention_mask:u}=e,n={input_ids:o,attention_mask:u??prepareAttentionMask(t,o)};const s=!!i;t.session.inputNames.includes("use_cache_branch")&&(n.use_cache_branch=boolTensor(s)),preparePositionIds(t.session,n,s),t.addPastKeyValues(n,i);let d=await sessionRun(t.session,n),f=d.logits;return i=t.getPastKeyValues(d,i),{logits:f,past_key_values:i}}function decoderStartBeams(t,e,o,i,u){let n=[],s=0;for(let d of e){let f=d.tolist().map(Number);d.dims=[1,...d.dims];let r;u?(r=u[s],r.dims=[1,...r.dims]):r=prepareAttentionMask(t,d);let a={input:d,model_input_ids:d,attention_mask:r,prev_model_outputs:null,output_token_ids:f,num_output_tokens:i,done:!1,score:0,id:s++};n.push(a)}return n}async function decoderRunBeam(t,e){let o=new BigInt64Array(e.output_token_ids.length).fill(1n),i={input_ids:e.model_input_ids,attention_mask:new Tensor("int64",o,[1,o.length]),past_key_values:e.prev_model_outputs?.past_key_values},u=await t.forward(i);return e.prev_model_outputs=u,u}function decoderUpdatebeam(t,e){t.output_token_ids=[...t.output_token_ids,e],t.model_input_ids=new Tensor("int64",[BigInt(e)],[1,1])}class PreTrainedModel extends Callable{main_input_name="input_ids";constructor(e,o){super(),this.config=e,this.session=o;const i=MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),u=MODEL_TYPE_MAPPING.get(i);this.can_generate=!1,this._runBeam=null,this._getStartBeams=null,this._updateBeam=null,this._forward=null,u===MODEL_TYPES.DecoderOnly?(this.can_generate=!0,this._runBeam=decoderRunBeam,this._getStartBeams=decoderStartBeams,this._updateBeam=decoderUpdatebeam,this._forward=decoderForward):u===MODEL_TYPES.Seq2Seq||u===MODEL_TYPES.Vision2Seq?(this.can_generate=!0,this._runBeam=seq2seqRunBeam,this._getStartBeams=seq2seqStartBeams,this._updateBeam=seq2seqUpdatebeam,this._forward=seq2seqForward):u===MODEL_TYPES.EncoderDecoder?this._forward=encoderForward:this._forward=encoderForward}async dispose(){const e=[];for(let o of Object.keys(this)){let i=this[o];i?.handler?.dispose!==void 0&&e.push(i.handler.dispose())}return await Promise.all(e)}static async from_pretrained(e,{quantized:o=!0,progress_callback:i=null,config:u=null,cache_dir:n=null,local_files_only:s=!1,revision:d="main",model_file_name:f=null,session_options:r={}}={}){let a={quantized:o,progress_callback:i,config:u,cache_dir:n,local_files_only:s,revision:d,model_file_name:f,session_options:r};const l=MODEL_CLASS_TO_NAME_MAPPING.get(this),p=MODEL_TYPE_MAPPING.get(l);let m;return p===MODEL_TYPES.DecoderOnly?m=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,a.model_file_name??"decoder_model_merged",a),getModelJSON(e,"generation_config.json",!1,a)]):p===MODEL_TYPES.Seq2Seq||p===MODEL_TYPES.Vision2Seq?m=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,"encoder_model",a),constructSession(e,"decoder_model_merged",a),getModelJSON(e,"generation_config.json",!1,a)]):p===MODEL_TYPES.MaskGeneration?m=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,"vision_encoder",a),constructSession(e,"prompt_encoder_mask_decoder",a)]):p===MODEL_TYPES.EncoderDecoder?m=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,"encoder_model",a),constructSession(e,"decoder_model_merged",a)]):(p!==MODEL_TYPES.EncoderOnly&&console.warn(`Model type for '${l??u?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`),m=await Promise.all([AutoConfig.from_pretrained(e,a),constructSession(e,a.model_file_name??"model",a)])),new this(...m)}async _call(e){return await this.forward(e)}async forward(e){return await this._forward(this,e)}_get_logits_processor(e,o,i=null){const u=new LogitsProcessorList;if(e.repetition_penalty!==null&&e.repetition_penalty!==1&&u.push(new RepetitionPenaltyLogitsProcessor(e.repetition_penalty)),e.no_repeat_ngram_size!==null&&e.no_repeat_ngram_size>0&&u.push(new NoRepeatNGramLogitsProcessor(e.no_repeat_ngram_size)),e.bad_words_ids!==null&&u.push(new NoBadWordsLogitsProcessor(e.bad_words_ids,e.eos_token_id)),e.min_length!==null&&e.eos_token_id!==null&&e.min_length>0&&u.push(new MinLengthLogitsProcessor(e.min_length,e.eos_token_id)),e.min_new_tokens!==null&&e.eos_token_id!==null&&e.min_new_tokens>0&&u.push(new MinNewTokensLengthLogitsProcessor(o,e.min_new_tokens,e.eos_token_id)),e.forced_bos_token_id!==null&&u.push(new ForcedBOSTokenLogitsProcessor(e.forced_bos_token_id)),e.forced_eos_token_id!==null&&u.push(new ForcedEOSTokenLogitsProcessor(e.max_length,e.forced_eos_token_id)),e.begin_suppress_tokens!==null){let n=o>1||e.forced_bos_token_id===null?o:o+1;e.forced_decoder_ids!==null&&(n+=e.forced_decoder_ids[e.forced_decoder_ids.length-1][0]),u.push(new SuppressTokensAtBeginLogitsProcessor(e.begin_suppress_tokens,n))}return e.forced_decoder_ids!==null&&u.push(new ForceTokensLogitsProcessor(e.forced_decoder_ids)),i!==null&&u.extend(i),u}_get_generation_config(e){let o=new GenerationConfig(this.config);return"generation_config"in this&&Object.assign(o,this.generation_config),e!==null&&Object.assign(o,e),o}async generate(e,o=null,i=null,{inputs_attention_mask:u=null}={}){if(!this.can_generate){let v=`The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;const S=this.config.model_type,E=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(S)??MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(S)??MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(S)??MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(S);throw E&&(v+=` Please use the following class instead: '${E[0]}'`),Error(v)}if(!(e instanceof Tensor)&&!isTypedArray(e)&&!Array.isArray(e))throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${e.constructor.name}".`);let n;if(this.config.is_encoder_decoder)n=0;else if(n=e instanceof Tensor?e.dims.at(-1):e.length,n===0)throw Error("Must supply a non-empty array of input token ids.");o=this._get_generation_config(o),i=i??new LogitsProcessorList,i=this._get_logits_processor(o,n,i);let s=o.eos_token_id;s!==null&&!Array.isArray(s)&&(s=[s]);let d=1;const f=d+(o.max_new_tokens??1/0),r=Number.isInteger(o.max_length)&&(o.max_new_tokens??null)===null;let a=Sampler.getSampler(o),l=this.getStartBeams(e,o,d,u);for(;l.some(T=>!T.done)&&d<f;){let T=[];for(let v of l){if(v.done){T.push(v);continue}if(r&&v.output_token_ids.length>=o.max_length){v.done=!0,T.push(v);continue}let S=await this.runBeam(v);o.output_attentions&&this.addAttentionsToBeam(v,S),o.output_scores;let E=S.logits.slice(null,-1,null);i(v.output_token_ids,E);let A=a(E);for(let[F,B]of A){let J={...v};this.updateBeam(J,F),J.score+=B,s&&s.includes(F)&&(J.done=!0),T.push(J)}}++d,T=this.groupBeams(T).map(v=>v.sort((S,E)=>E.score-S.score).slice(0,o.num_beams)),l=T.flat(),o.callback_function&&o.callback_function(l)}const p=this.groupBeams(l),m=T=>p.map(v=>o.num_return_sequences>1?v.slice(0,o.num_return_sequences).map(S=>S[T]):[v[0][T]]).flat(),y=m("output_token_ids");if(o.return_dict_in_generate){const T=m("decoder_attentions"),v=m("cross_attentions");return{sequences:y,decoder_attentions:T,cross_attentions:v}}else return y}addAttentionsToBeam(e,o){if(this.config.is_encoder_decoder){if(!o.cross_attentions||o.cross_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.cross_attentions||(e.cross_attentions=[]),e.cross_attentions.push(o.cross_attentions)}if(!o.decoder_attentions||o.decoder_attentions.length===0)throw Error("`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`.");e.decoder_attentions||(e.decoder_attentions=[]),e.decoder_attentions.push(o.decoder_attentions)}groupBeams(e){const o=Object.create(null);for(const i of e)o[i.id]===void 0?o[i.id]=[i]:o[i.id].push(i);return Object.values(o)}getPastKeyValues(e,o){const i=Object.create(null);for(const u in e)if(u.startsWith("present")){let n=u.replace("present","past_key_values");o&&u.includes("encoder")?i[n]=o[n]:i[n]=e[u]}return i}getAttentions(e){const o=Object.create(null);for(const i of["cross_attentions","decoder_attentions"]){const u=[];for(const n in e)if(n.startsWith(i)){const s=n.split(".").pop();u[s]=e[n]}o[i]=u}return o}addPastKeyValues(e,o){if(o)Object.assign(e,o);else{const u=this.config.precision||"float32",n=u==="float16"?new Uint16Array:[];if(this.config.is_encoder_decoder&&(this.add_encoder_pkv??!0)){let s=[1,this.num_encoder_heads,0,this.encoder_dim_kv],d=[1,this.num_decoder_heads,0,this.decoder_dim_kv];for(let f=0;f<this.num_decoder_layers;++f)e[`past_key_values.${f}.encoder.key`]=new Tensor(u,n,s),e[`past_key_values.${f}.encoder.value`]=new Tensor(u,n,s),e[`past_key_values.${f}.decoder.key`]=new Tensor(u,n,d),e[`past_key_values.${f}.decoder.value`]=new Tensor(u,n,d)}else if(this.config.model_type==="falcon"){let s=[1*this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)e[`past_key_values.${d}.key`]=new Tensor(u,n,s),e[`past_key_values.${d}.value`]=new Tensor(u,n,s)}else if(this.config.multi_query){let s=[1*this.num_heads,0,2*this.dim_kv];for(let d=0;d<this.num_layers;++d)e[`past_key_values.${d}.key_value`]=new Tensor(u,n,s)}else if(this.config.model_type==="bloom"){let s=[1*this.num_heads,this.dim_kv,0],d=[1*this.num_heads,0,this.dim_kv];for(let f=0;f<this.num_layers;++f)e[`past_key_values.${f}.key`]=new Tensor(u,n,s),e[`past_key_values.${f}.value`]=new Tensor(u,n,d)}else{let s=[1,this.num_heads,0,this.dim_kv];for(let d=0;d<this.num_layers;++d)e[`past_key_values.${d}.key`]=new Tensor(u,n,s),e[`past_key_values.${d}.value`]=new Tensor(u,n,s)}}}getStartBeams(e,o,i,u){return this._getStartBeams(this,e,o,i,u)}async runBeam(e){return await this._runBeam(this,e)}updateBeam(e,o){return this._updateBeam(e,o)}}class ModelOutput{}class BertPreTrainedModel extends PreTrainedModel{}class BertModel extends BertPreTrainedModel{}class BertForMaskedLM extends BertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class BertForSequenceClassification extends BertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class BertForTokenClassification extends BertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class BertForQuestionAnswering extends BertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class NomicBertPreTrainedModel extends PreTrainedModel{}class NomicBertModel extends NomicBertPreTrainedModel{}class RoFormerPreTrainedModel extends PreTrainedModel{}class RoFormerModel extends RoFormerPreTrainedModel{}class RoFormerForMaskedLM extends RoFormerPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class RoFormerForSequenceClassification extends RoFormerPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class RoFormerForTokenClassification extends RoFormerPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class ConvBertPreTrainedModel extends PreTrainedModel{}class ConvBertModel extends ConvBertPreTrainedModel{}class ConvBertForMaskedLM extends ConvBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class ConvBertForSequenceClassification extends ConvBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class ConvBertForTokenClassification extends ConvBertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class ElectraPreTrainedModel extends PreTrainedModel{}class ElectraModel extends ElectraPreTrainedModel{}class ElectraForMaskedLM extends ElectraPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class ElectraForSequenceClassification extends ElectraPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class ElectraForTokenClassification extends ElectraPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class ElectraForQuestionAnswering extends ElectraPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class CamembertPreTrainedModel extends PreTrainedModel{}class CamembertModel extends CamembertPreTrainedModel{}class CamembertForMaskedLM extends CamembertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class CamembertForSequenceClassification extends CamembertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class CamembertForTokenClassification extends CamembertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class CamembertForQuestionAnswering extends CamembertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DebertaPreTrainedModel extends PreTrainedModel{}class DebertaModel extends DebertaPreTrainedModel{}class DebertaForMaskedLM extends DebertaPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class DebertaForSequenceClassification extends DebertaPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DebertaForTokenClassification extends DebertaPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class DebertaForQuestionAnswering extends DebertaPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DebertaV2PreTrainedModel extends PreTrainedModel{}class DebertaV2Model extends DebertaV2PreTrainedModel{}class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DistilBertPreTrainedModel extends PreTrainedModel{}class DistilBertModel extends DistilBertPreTrainedModel{}class DistilBertForSequenceClassification extends DistilBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DistilBertForTokenClassification extends DistilBertPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class DistilBertForMaskedLM extends DistilBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class EsmPreTrainedModel extends PreTrainedModel{}class EsmModel extends EsmPreTrainedModel{}class EsmForMaskedLM extends EsmPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class EsmForSequenceClassification extends EsmPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class EsmForTokenClassification extends EsmPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class MobileBertPreTrainedModel extends PreTrainedModel{}class MobileBertModel extends MobileBertPreTrainedModel{}class MobileBertForMaskedLM extends MobileBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class MobileBertForSequenceClassification extends MobileBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class MPNetPreTrainedModel extends PreTrainedModel{}class MPNetModel extends MPNetPreTrainedModel{}class MPNetForMaskedLM extends MPNetPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class MPNetForSequenceClassification extends MPNetPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MPNetForTokenClassification extends MPNetPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class MPNetForQuestionAnswering extends MPNetPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class SqueezeBertPreTrainedModel extends PreTrainedModel{}class SqueezeBertModel extends SqueezeBertPreTrainedModel{}class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class AlbertPreTrainedModel extends PreTrainedModel{}class AlbertModel extends AlbertPreTrainedModel{}class AlbertForSequenceClassification extends AlbertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class AlbertForQuestionAnswering extends AlbertPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class AlbertForMaskedLM extends AlbertPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class T5PreTrainedModel extends PreTrainedModel{}class T5Model extends T5PreTrainedModel{}class T5ForConditionalGeneration extends T5PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class LongT5PreTrainedModel extends PreTrainedModel{}class LongT5Model extends LongT5PreTrainedModel{}class LongT5ForConditionalGeneration extends LongT5PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class MT5PreTrainedModel extends PreTrainedModel{}class MT5Model extends MT5PreTrainedModel{}class MT5ForConditionalGeneration extends MT5PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.num_decoder_layers,this.num_decoder_heads=this.config.num_heads,this.decoder_dim_kv=this.config.d_kv,this.num_encoder_layers=this.config.num_layers,this.num_encoder_heads=this.config.num_heads,this.encoder_dim_kv=this.config.d_kv}}class BartPretrainedModel extends PreTrainedModel{}class BartModel extends BartPretrainedModel{}class BartForConditionalGeneration extends BartPretrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BartForSequenceClassification extends BartPretrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MBartPreTrainedModel extends PreTrainedModel{}class MBartModel extends MBartPreTrainedModel{}class MBartForConditionalGeneration extends MBartPreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class MBartForSequenceClassification extends MBartPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class MBartForCausalLM extends MBartPreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BlenderbotPreTrainedModel extends PreTrainedModel{}class BlenderbotModel extends BlenderbotPreTrainedModel{}class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class BlenderbotSmallPreTrainedModel extends PreTrainedModel{}class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel{}class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class RobertaPreTrainedModel extends PreTrainedModel{}class RobertaModel extends RobertaPreTrainedModel{}class RobertaForMaskedLM extends RobertaPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class RobertaForSequenceClassification extends RobertaPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class RobertaForTokenClassification extends RobertaPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class RobertaForQuestionAnswering extends RobertaPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class XLMPreTrainedModel extends PreTrainedModel{}class XLMModel extends XLMPreTrainedModel{}class XLMWithLMHeadModel extends XLMPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class XLMForSequenceClassification extends XLMPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class XLMForTokenClassification extends XLMPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class XLMForQuestionAnswering extends XLMPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class XLMRobertaPreTrainedModel extends PreTrainedModel{}class XLMRobertaModel extends XLMRobertaPreTrainedModel{}class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel{async _call(e){return new MaskedLMOutput(await super._call(e))}}class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel{async _call(e){return new QuestionAnsweringModelOutput(await super._call(e))}}class ASTPreTrainedModel extends PreTrainedModel{}class ASTModel extends ASTPreTrainedModel{}class ASTForAudioClassification extends ASTPreTrainedModel{}class WhisperPreTrainedModel extends PreTrainedModel{}class WhisperModel extends WhisperPreTrainedModel{}class WhisperForConditionalGeneration extends WhisperPreTrainedModel{requires_attention_mask=!1;main_input_name="input_features";constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}async generate(e,o=null,i=null){if(o=this._get_generation_config(o),o.return_timestamps??=!1,o.return_timestamps&&(i=[new WhisperTimeStampLogitsProcessor(o)]),o.return_token_timestamps&&(o.output_attentions=!0,o.return_dict_in_generate=!0,o.task==="translate"&&console.warn("Token-level timestamps may not be reliable for task 'translate'."),!o.alignment_heads))throw new Error("Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config.");const u=await super.generate(e,o,i);return o.return_token_timestamps&&o.alignment_heads&&(u.token_timestamps=this._extract_token_timestamps(u,o.alignment_heads,o.num_frames)),u}_extract_token_timestamps(e,o,i=null,u=.02){if(!e.cross_attentions)throw new Error("Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`.");let n=this.config.median_filter_width;n===void 0&&(console.warn("Model config has no `median_filter_width`, using default value of 7."),n=7);const s=e.cross_attentions.map(r=>{let a=Array.from({length:this.config.decoder_layers},(v,S)=>cat(r.map(E=>E[S]),2)),l=stack(o.map(([v,S])=>i?a[v].slice(null,S,null,[0,i]):a[v].slice(null,S)));l=l.transpose(1,0,2,3);let[p,m]=std_mean(l,-2,0,!0),y=l.clone();for(let v=0;v<y.dims[0];++v){let S=y[v];for(let E=0;E<S.dims[0];++E){let A=S[E];const F=p[v][E][0],B=m[v][E][0];for(let J=0;J<A.dims[0];++J){let Q=A[J];for(let re=0;re<Q.data.length;++re)Q.data[re]=(Q.data[re]-B.data[re])/F.data[re];Q.data.set(medianFilter(Q.data,n))}}}return mean(y,1)}),d=[e.sequences.length,e.sequences[0].length],f=new Tensor("float32",new Float32Array(d[0]*d[1]),d);for(let r=0;r<d[0];++r){const a=s[r].neg().squeeze_(0);let[l,p]=dynamicTimeWarping(a),m=Array.from({length:l.length-1},(v,S)=>l[S+1]-l[S]),y=mergeArrays([1],m).map(v=>!!v),T=[];for(let v=0;v<y.length;++v)y[v]&&T.push(p[v]*u);f[r].data.set(T,1)}return f}}class VisionEncoderDecoderModel extends PreTrainedModel{main_input_name="pixel_values";constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u;const n=this.config.encoder,s=this.config.decoder,d=n.model_type;(MODEL_MAPPING_NAMES_ENCODER_ONLY.get(d)??MODEL_MAPPING_NAMES_ENCODER_DECODER.get(d))||console.warn(`Model type for encoder '${d}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);const r=MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(s.model_type);if(!r)throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);const a=r[1],l=new a(s,i,u);this.add_encoder_pkv="num_decoder_layers"in l,this.add_encoder_pkv?(this.num_decoder_layers=l.num_decoder_layers,this.num_decoder_heads=l.num_decoder_heads,this.decoder_dim_kv=l.decoder_dim_kv,this.num_encoder_layers=l.num_encoder_layers,this.num_encoder_heads=l.num_encoder_heads,this.encoder_dim_kv=l.encoder_dim_kv):(this.num_layers=l.num_layers,this.num_heads=l.num_heads,this.dim_kv=l.dim_kv)}}class CLIPPreTrainedModel extends PreTrainedModel{}class CLIPModel extends CLIPPreTrainedModel{}class CLIPTextModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="text_model",super.from_pretrained(e,o)}}class CLIPVisionModelWithProjection extends CLIPPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="vision_model",super.from_pretrained(e,o)}}class SiglipPreTrainedModel extends PreTrainedModel{}class SiglipModel extends SiglipPreTrainedModel{}class SiglipTextModel extends SiglipPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="text_model",super.from_pretrained(e,o)}}class SiglipVisionModel extends CLIPPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="vision_model",super.from_pretrained(e,o)}}class ChineseCLIPPreTrainedModel extends PreTrainedModel{}class ChineseCLIPModel extends ChineseCLIPPreTrainedModel{}class CLIPSegPreTrainedModel extends PreTrainedModel{}class CLIPSegModel extends CLIPSegPreTrainedModel{}class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel{}class GPT2PreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPT2Model extends GPT2PreTrainedModel{}class GPT2LMHeadModel extends GPT2PreTrainedModel{}class GPTNeoPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_heads,this.num_layers=this.config.num_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class GPTNeoModel extends GPTNeoPreTrainedModel{}class GPTNeoForCausalLM extends GPTNeoPreTrainedModel{}class GPTNeoXPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class GPTNeoXModel extends GPTNeoXPreTrainedModel{}class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel{}class GPTJPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPTJModel extends GPTJPreTrainedModel{}class GPTJForCausalLM extends GPTJPreTrainedModel{}class GPTBigCodePreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class GPTBigCodeModel extends GPTBigCodePreTrainedModel{}class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel{}class CodeGenPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.n_embd/this.num_heads}}class CodeGenModel extends CodeGenPreTrainedModel{}class CodeGenForCausalLM extends CodeGenPreTrainedModel{}class LlamaPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class LlamaModel extends LlamaPreTrainedModel{}class LlamaForCausalLM extends LlamaPreTrainedModel{}class Qwen2PreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads??this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class Qwen2Model extends Qwen2PreTrainedModel{}class Qwen2ForCausalLM extends Qwen2PreTrainedModel{}class PhiPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class PhiModel extends PhiPreTrainedModel{}class PhiForCausalLM extends PhiPreTrainedModel{}class BloomPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_head,this.num_layers=this.config.n_layer,this.dim_kv=this.config.hidden_size/this.num_heads}}class BloomModel extends BloomPreTrainedModel{}class BloomForCausalLM extends BloomPreTrainedModel{}class MptPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.n_heads,this.num_layers=this.config.n_layers,this.dim_kv=this.config.d_model/this.num_heads}}class MptModel extends MptPreTrainedModel{}class MptForCausalLM extends MptPreTrainedModel{}class OPTPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class OPTModel extends OPTPreTrainedModel{}class OPTForCausalLM extends OPTPreTrainedModel{}class ViTPreTrainedModel extends PreTrainedModel{}class ViTModel extends ViTPreTrainedModel{}class ViTForImageClassification extends ViTPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class VitMattePreTrainedModel extends PreTrainedModel{}class VitMatteForImageMatting extends VitMattePreTrainedModel{async _call(e){return new ImageMattingOutput(await super._call(e))}}class MobileViTPreTrainedModel extends PreTrainedModel{}class MobileViTModel extends MobileViTPreTrainedModel{}class MobileViTForImageClassification extends MobileViTPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class OwlViTPreTrainedModel extends PreTrainedModel{}class OwlViTModel extends OwlViTPreTrainedModel{}class OwlViTForObjectDetection extends OwlViTPreTrainedModel{}class Owlv2PreTrainedModel extends PreTrainedModel{}class Owlv2Model extends Owlv2PreTrainedModel{}class Owlv2ForObjectDetection extends Owlv2PreTrainedModel{}class BeitPreTrainedModel extends PreTrainedModel{}class BeitModel extends BeitPreTrainedModel{}class BeitForImageClassification extends BeitPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class DetrPreTrainedModel extends PreTrainedModel{}class DetrModel extends DetrPreTrainedModel{}class DetrForObjectDetection extends DetrPreTrainedModel{async _call(e){return new DetrObjectDetectionOutput(await super._call(e))}}class DetrForSegmentation extends DetrPreTrainedModel{async _call(e){return new DetrSegmentationOutput(await super._call(e))}}class DetrObjectDetectionOutput extends ModelOutput{constructor({logits:e,pred_boxes:o}){super(),this.logits=e,this.pred_boxes=o}}class DetrSegmentationOutput extends ModelOutput{constructor({logits:e,pred_boxes:o,pred_masks:i}){super(),this.logits=e,this.pred_boxes=o,this.pred_masks=i}}class TableTransformerPreTrainedModel extends PreTrainedModel{}class TableTransformerModel extends TableTransformerPreTrainedModel{}class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel{async _call(e){return new TableTransformerObjectDetectionOutput(await super._call(e))}}class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput{}class DeiTPreTrainedModel extends PreTrainedModel{}class DeiTModel extends DeiTPreTrainedModel{}class DeiTForImageClassification extends DeiTPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class ResNetPreTrainedModel extends PreTrainedModel{}class ResNetModel extends ResNetPreTrainedModel{}class ResNetForImageClassification extends ResNetPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class SwinPreTrainedModel extends PreTrainedModel{}class SwinModel extends SwinPreTrainedModel{}class SwinForImageClassification extends SwinPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class Swin2SRPreTrainedModel extends PreTrainedModel{}class Swin2SRModel extends Swin2SRPreTrainedModel{}class Swin2SRForImageSuperResolution extends Swin2SRPreTrainedModel{}class DPTPreTrainedModel extends PreTrainedModel{}class DPTModel extends DPTPreTrainedModel{}class DPTForDepthEstimation extends DPTPreTrainedModel{}class DepthAnythingPreTrainedModel extends PreTrainedModel{}class DepthAnythingForDepthEstimation extends DepthAnythingPreTrainedModel{}class GLPNPreTrainedModel extends PreTrainedModel{}class GLPNModel extends GLPNPreTrainedModel{}class GLPNForDepthEstimation extends GLPNPreTrainedModel{}class DonutSwinPreTrainedModel extends PreTrainedModel{}class DonutSwinModel extends DonutSwinPreTrainedModel{}class ConvNextPreTrainedModel extends PreTrainedModel{}class ConvNextModel extends ConvNextPreTrainedModel{}class ConvNextForImageClassification extends ConvNextPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class ConvNextV2PreTrainedModel extends PreTrainedModel{}class ConvNextV2Model extends ConvNextV2PreTrainedModel{}class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class Dinov2PreTrainedModel extends PreTrainedModel{}class Dinov2Model extends Dinov2PreTrainedModel{}class Dinov2ForImageClassification extends Dinov2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class YolosPreTrainedModel extends PreTrainedModel{}class YolosModel extends YolosPreTrainedModel{}class YolosForObjectDetection extends YolosPreTrainedModel{async _call(e){return new YolosObjectDetectionOutput(await super._call(e))}}class YolosObjectDetectionOutput extends ModelOutput{constructor({logits:e,pred_boxes:o}){super(),this.logits=e,this.pred_boxes=o}}class SamPreTrainedModel extends PreTrainedModel{}class SamModel extends SamPreTrainedModel{constructor(e,o,i){super(e,o),this.prompt_encoder_mask_decoder=i}async get_image_embeddings({pixel_values:e}){return await encoderForward(this,{pixel_values:e})}async forward(e){if((!e.image_embeddings||!e.image_positional_embeddings)&&(e={...e,...await this.get_image_embeddings(e)}),!e.input_labels){const o=e.input_points.dims.slice(0,-1),i=o.reduce((u,n)=>u*n,1);e.input_labels=new Tensor("int64",new BigInt64Array(i).fill(1n),o)}return await sessionRun(this.prompt_encoder_mask_decoder,{input_points:e.input_points,input_labels:e.input_labels,image_embeddings:e.image_embeddings,image_positional_embeddings:e.image_positional_embeddings})}async _call(e){return new SamImageSegmentationOutput(await super._call(e))}}class SamImageSegmentationOutput extends ModelOutput{constructor({iou_scores:e,pred_masks:o}){super(),this.iou_scores=e,this.pred_masks=o}}class MarianPreTrainedModel extends PreTrainedModel{}class MarianModel extends MarianPreTrainedModel{}class MarianMTModel extends MarianPreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class M2M100PreTrainedModel extends PreTrainedModel{}class M2M100Model extends M2M100PreTrainedModel{}class M2M100ForConditionalGeneration extends M2M100PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.d_model/this.num_encoder_heads}}class Wav2Vec2PreTrainedModel extends PreTrainedModel{}class Wav2Vec2Model extends Wav2Vec2PreTrainedModel{}class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class UniSpeechPreTrainedModel extends PreTrainedModel{}class UniSpeechModel extends UniSpeechPreTrainedModel{}class UniSpeechForCTC extends UniSpeechPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class UniSpeechForSequenceClassification extends UniSpeechPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class UniSpeechSatPreTrainedModel extends PreTrainedModel{}class UniSpeechSatModel extends UniSpeechSatPreTrainedModel{}class UniSpeechSatForCTC extends UniSpeechSatPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class UniSpeechSatForSequenceClassification extends UniSpeechSatPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class UniSpeechSatForAudioFrameClassification extends UniSpeechSatPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class Wav2Vec2BertPreTrainedModel extends PreTrainedModel{}class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel{}class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class HubertModel extends Wav2Vec2PreTrainedModel{}class HubertForCTC extends Wav2Vec2PreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class WavLMPreTrainedModel extends PreTrainedModel{}class WavLMModel extends WavLMPreTrainedModel{}class WavLMForCTC extends WavLMPreTrainedModel{async _call(e){return new CausalLMOutput(await super._call(e))}}class WavLMForSequenceClassification extends WavLMPreTrainedModel{async _call(e){return new SequenceClassifierOutput(await super._call(e))}}class WavLMForXVector extends WavLMPreTrainedModel{async _call(e){return new XVectorOutput(await super._call(e))}}class WavLMForAudioFrameClassification extends WavLMPreTrainedModel{async _call(e){return new TokenClassifierOutput(await super._call(e))}}class SpeechT5PreTrainedModel extends PreTrainedModel{}class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel{}class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel{constructor(e,o,i,u){super(e,o),this.decoder_merged_session=i,this.generation_config=u,this.num_decoder_layers=this.config.decoder_layers,this.num_decoder_heads=this.config.decoder_attention_heads,this.decoder_dim_kv=this.config.hidden_size/this.num_decoder_heads,this.num_encoder_layers=this.config.encoder_layers,this.num_encoder_heads=this.config.encoder_attention_heads,this.encoder_dim_kv=this.config.hidden_size/this.num_encoder_heads}async generate_speech(e,o,{threshold:i=.5,minlenratio:u=0,maxlenratio:n=20,vocoder:s=null}={}){const d={input_ids:e},{encoder_outputs:f,encoder_attention_mask:r}=await encoderForward(this,d),a=f.dims[1]/this.config.reduction_factor,l=Math.floor(a*n),p=Math.floor(a*u),m=this.config.num_mel_bins;let y=[],T=null,v=null,S=0;for(;;){++S;const F=boolTensor(!!v);let B;v?B=v.output_sequence_out:B=new Tensor("float32",new Float32Array(m),[1,1,m]);let J={use_cache_branch:F,output_sequence:B,encoder_attention_mask:r,speaker_embeddings:o,encoder_hidden_states:f};this.addPastKeyValues(J,T),v=await sessionRun(this.decoder_merged_session,J),T=this.getPastKeyValues(v,T);const{prob:Q,spectrum:re}=v;if(y.push(re),S>=p&&(Array.from(Q.data).filter(V=>V>=i).length>0||S>=l))break}const E=cat(y),{waveform:A}=await sessionRun(s.session,{spectrogram:E});return{spectrogram:E,waveform:A}}}class SpeechT5HifiGan extends PreTrainedModel{main_input_name="spectrogram"}class TrOCRPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_encoder_layers=this.num_decoder_layers=this.config.decoder_layers,this.num_encoder_heads=this.num_decoder_heads=this.config.decoder_attention_heads,this.encoder_dim_kv=this.decoder_dim_kv=this.config.d_model/this.num_decoder_heads}}class TrOCRForCausalLM extends TrOCRPreTrainedModel{}class MistralPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class MistralModel extends MistralPreTrainedModel{}class MistralForCausalLM extends MistralPreTrainedModel{}class Starcoder2PreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_key_value_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class Starcoder2Model extends Starcoder2PreTrainedModel{}class Starcoder2ForCausalLM extends Starcoder2PreTrainedModel{}class FalconPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.config.num_attention_heads}}class FalconModel extends FalconPreTrainedModel{}class FalconForCausalLM extends FalconPreTrainedModel{}class ClapPreTrainedModel extends PreTrainedModel{}class ClapModel extends ClapPreTrainedModel{}class ClapTextModelWithProjection extends ClapPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="text_model",super.from_pretrained(e,o)}}class ClapAudioModelWithProjection extends ClapPreTrainedModel{static async from_pretrained(e,o={}){return o.model_file_name??="audio_model",super.from_pretrained(e,o)}}class VitsPreTrainedModel extends PreTrainedModel{}class VitsModel extends VitsPreTrainedModel{async _call(e){return new VitsModelOutput(await super._call(e))}}class SegformerPreTrainedModel extends PreTrainedModel{}class SegformerForImageClassification extends SegformerPreTrainedModel{}class SegformerForSemanticSegmentation extends SegformerPreTrainedModel{}class StableLmPreTrainedModel extends PreTrainedModel{constructor(e,o,i){super(e,o),this.generation_config=i,this.config.pad_token_id=this.config.eos_token_id,this.num_heads=this.config.num_attention_heads,this.num_layers=this.config.num_hidden_layers,this.dim_kv=this.config.hidden_size/this.num_heads}}class StableLmForCausalLM extends StableLmPreTrainedModel{}class PretrainedMixin{static MODEL_CLASS_MAPPINGS=null;static BASE_IF_FAIL=!1;static async from_pretrained(e,{quantized:o=!0,progress_callback:i=null,config:u=null,cache_dir:n=null,local_files_only:s=!1,revision:d="main",model_file_name:f=null,session_options:r={}}={}){let a={quantized:o,progress_callback:i,config:u,cache_dir:n,local_files_only:s,revision:d,model_file_name:f,session_options:r};if(u=await AutoConfig.from_pretrained(e,a),a.config||(a.config=u),!this.MODEL_CLASS_MAPPINGS)throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: "+this.name);for(let l of this.MODEL_CLASS_MAPPINGS){const p=l.get(u.model_type);if(p)return await p[1].from_pretrained(e,a)}if(this.BASE_IF_FAIL)return console.warn(`Unknown model class "${u.model_type}", attempting to construct from base class.`),await PreTrainedModel.from_pretrained(e,a);throw Error(`Unsupported model type: ${u.model_type}`)}}const MODEL_MAPPING_NAMES_ENCODER_ONLY=new Map([["bert",["BertModel",BertModel]],["nomic_bert",["NomicBertModel",NomicBertModel]],["roformer",["RoFormerModel",RoFormerModel]],["electra",["ElectraModel",ElectraModel]],["esm",["EsmModel",EsmModel]],["convbert",["ConvBertModel",ConvBertModel]],["camembert",["CamembertModel",CamembertModel]],["deberta",["DebertaModel",DebertaModel]],["deberta-v2",["DebertaV2Model",DebertaV2Model]],["mpnet",["MPNetModel",MPNetModel]],["albert",["AlbertModel",AlbertModel]],["distilbert",["DistilBertModel",DistilBertModel]],["roberta",["RobertaModel",RobertaModel]],["xlm",["XLMModel",XLMModel]],["xlm-roberta",["XLMRobertaModel",XLMRobertaModel]],["clap",["ClapModel",ClapModel]],["clip",["CLIPModel",CLIPModel]],["clipseg",["CLIPSegModel",CLIPSegModel]],["chinese_clip",["ChineseCLIPModel",ChineseCLIPModel]],["siglip",["SiglipModel",SiglipModel]],["mobilebert",["MobileBertModel",MobileBertModel]],["squeezebert",["SqueezeBertModel",SqueezeBertModel]],["wav2vec2",["Wav2Vec2Model",Wav2Vec2Model]],["wav2vec2-bert",["Wav2Vec2BertModel",Wav2Vec2BertModel]],["unispeech",["UniSpeechModel",UniSpeechModel]],["unispeech-sat",["UniSpeechSatModel",UniSpeechSatModel]],["hubert",["HubertModel",HubertModel]],["wavlm",["WavLMModel",WavLMModel]],["audio-spectrogram-transformer",["ASTModel",ASTModel]],["vits",["VitsModel",VitsModel]],["detr",["DetrModel",DetrModel]],["table-transformer",["TableTransformerModel",TableTransformerModel]],["vit",["ViTModel",ViTModel]],["mobilevit",["MobileViTModel",MobileViTModel]],["owlvit",["OwlViTModel",OwlViTModel]],["owlv2",["Owlv2Model",Owlv2Model]],["beit",["BeitModel",BeitModel]],["deit",["DeiTModel",DeiTModel]],["convnext",["ConvNextModel",ConvNextModel]],["convnextv2",["ConvNextV2Model",ConvNextV2Model]],["dinov2",["Dinov2Model",Dinov2Model]],["resnet",["ResNetModel",ResNetModel]],["swin",["SwinModel",SwinModel]],["swin2sr",["Swin2SRModel",Swin2SRModel]],["donut-swin",["DonutSwinModel",DonutSwinModel]],["yolos",["YolosModel",YolosModel]],["dpt",["DPTModel",DPTModel]],["glpn",["GLPNModel",GLPNModel]],["hifigan",["SpeechT5HifiGan",SpeechT5HifiGan]]]),MODEL_MAPPING_NAMES_ENCODER_DECODER=new Map([["t5",["T5Model",T5Model]],["longt5",["LongT5Model",LongT5Model]],["mt5",["MT5Model",MT5Model]],["bart",["BartModel",BartModel]],["mbart",["MBartModel",MBartModel]],["marian",["MarianModel",MarianModel]],["whisper",["WhisperModel",WhisperModel]],["m2m_100",["M2M100Model",M2M100Model]],["blenderbot",["BlenderbotModel",BlenderbotModel]],["blenderbot-small",["BlenderbotSmallModel",BlenderbotSmallModel]]]),MODEL_MAPPING_NAMES_DECODER_ONLY=new Map([["bloom",["BloomModel",BloomModel]],["gpt2",["GPT2Model",GPT2Model]],["gptj",["GPTJModel",GPTJModel]],["gpt_bigcode",["GPTBigCodeModel",GPTBigCodeModel]],["gpt_neo",["GPTNeoModel",GPTNeoModel]],["gpt_neox",["GPTNeoXModel",GPTNeoXModel]],["codegen",["CodeGenModel",CodeGenModel]],["llama",["LlamaModel",LlamaModel]],["qwen2",["Qwen2Model",Qwen2Model]],["phi",["PhiModel",PhiModel]],["mpt",["MptModel",MptModel]],["opt",["OPTModel",OPTModel]],["mistral",["MistralModel",MistralModel]],["starcoder2",["Starcoder2Model",Starcoder2Model]],["falcon",["FalconModel",FalconModel]]]),MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES=new Map([["speecht5",["SpeechT5ForSpeechToText",SpeechT5ForSpeechToText]],["whisper",["WhisperForConditionalGeneration",WhisperForConditionalGeneration]]]),MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES=new Map([["speecht5",["SpeechT5ForTextToSpeech",SpeechT5ForTextToSpeech]]]),MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES=new Map([["vits",["VitsModel",VitsModel]]]),MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",["BertForSequenceClassification",BertForSequenceClassification]],["roformer",["RoFormerForSequenceClassification",RoFormerForSequenceClassification]],["electra",["ElectraForSequenceClassification",ElectraForSequenceClassification]],["esm",["EsmForSequenceClassification",EsmForSequenceClassification]],["convbert",["ConvBertForSequenceClassification",ConvBertForSequenceClassification]],["camembert",["CamembertForSequenceClassification",CamembertForSequenceClassification]],["deberta",["DebertaForSequenceClassification",DebertaForSequenceClassification]],["deberta-v2",["DebertaV2ForSequenceClassification",DebertaV2ForSequenceClassification]],["mpnet",["MPNetForSequenceClassification",MPNetForSequenceClassification]],["albert",["AlbertForSequenceClassification",AlbertForSequenceClassification]],["distilbert",["DistilBertForSequenceClassification",DistilBertForSequenceClassification]],["roberta",["RobertaForSequenceClassification",RobertaForSequenceClassification]],["xlm",["XLMForSequenceClassification",XLMForSequenceClassification]],["xlm-roberta",["XLMRobertaForSequenceClassification",XLMRobertaForSequenceClassification]],["bart",["BartForSequenceClassification",BartForSequenceClassification]],["mbart",["MBartForSequenceClassification",MBartForSequenceClassification]],["mobilebert",["MobileBertForSequenceClassification",MobileBertForSequenceClassification]],["squeezebert",["SqueezeBertForSequenceClassification",SqueezeBertForSequenceClassification]]]),MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES=new Map([["bert",["BertForTokenClassification",BertForTokenClassification]],["roformer",["RoFormerForTokenClassification",RoFormerForTokenClassification]],["electra",["ElectraForTokenClassification",ElectraForTokenClassification]],["esm",["EsmForTokenClassification",EsmForTokenClassification]],["convbert",["ConvBertForTokenClassification",ConvBertForTokenClassification]],["camembert",["CamembertForTokenClassification",CamembertForTokenClassification]],["deberta",["DebertaForTokenClassification",DebertaForTokenClassification]],["deberta-v2",["DebertaV2ForTokenClassification",DebertaV2ForTokenClassification]],["mpnet",["MPNetForTokenClassification",MPNetForTokenClassification]],["distilbert",["DistilBertForTokenClassification",DistilBertForTokenClassification]],["roberta",["RobertaForTokenClassification",RobertaForTokenClassification]],["xlm",["XLMForTokenClassification",XLMForTokenClassification]],["xlm-roberta",["XLMRobertaForTokenClassification",XLMRobertaForTokenClassification]]]),MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES=new Map([["t5",["T5ForConditionalGeneration",T5ForConditionalGeneration]],["longt5",["LongT5ForConditionalGeneration",LongT5ForConditionalGeneration]],["mt5",["MT5ForConditionalGeneration",MT5ForConditionalGeneration]],["bart",["BartForConditionalGeneration",BartForConditionalGeneration]],["mbart",["MBartForConditionalGeneration",MBartForConditionalGeneration]],["marian",["MarianMTModel",MarianMTModel]],["m2m_100",["M2M100ForConditionalGeneration",M2M100ForConditionalGeneration]],["blenderbot",["BlenderbotForConditionalGeneration",BlenderbotForConditionalGeneration]],["blenderbot-small",["BlenderbotSmallForConditionalGeneration",BlenderbotSmallForConditionalGeneration]]]),MODEL_WITH_LM_HEAD_MAPPING_NAMES=new Map([["bloom",["BloomForCausalLM",BloomForCausalLM]],["gpt2",["GPT2LMHeadModel",GPT2LMHeadModel]],["gptj",["GPTJForCausalLM",GPTJForCausalLM]],["gpt_bigcode",["GPTBigCodeForCausalLM",GPTBigCodeForCausalLM]],["gpt_neo",["GPTNeoForCausalLM",GPTNeoForCausalLM]],["gpt_neox",["GPTNeoXForCausalLM",GPTNeoXForCausalLM]],["codegen",["CodeGenForCausalLM",CodeGenForCausalLM]],["llama",["LlamaForCausalLM",LlamaForCausalLM]],["qwen2",["Qwen2ForCausalLM",Qwen2ForCausalLM]],["phi",["PhiForCausalLM",PhiForCausalLM]],["mpt",["MptForCausalLM",MptForCausalLM]],["opt",["OPTForCausalLM",OPTForCausalLM]],["mbart",["MBartForCausalLM",MBartForCausalLM]],["mistral",["MistralForCausalLM",MistralForCausalLM]],["starcoder2",["Starcoder2ForCausalLM",Starcoder2ForCausalLM]],["falcon",["FalconForCausalLM",FalconForCausalLM]],["trocr",["TrOCRForCausalLM",TrOCRForCausalLM]],["stablelm",["StableLmForCausalLM",StableLmForCausalLM]]]),MODEL_FOR_MASKED_LM_MAPPING_NAMES=new Map([["bert",["BertForMaskedLM",BertForMaskedLM]],["roformer",["RoFormerForMaskedLM",RoFormerForMaskedLM]],["electra",["ElectraForMaskedLM",ElectraForMaskedLM]],["esm",["EsmForMaskedLM",EsmForMaskedLM]],["convbert",["ConvBertForMaskedLM",ConvBertForMaskedLM]],["camembert",["CamembertForMaskedLM",CamembertForMaskedLM]],["deberta",["DebertaForMaskedLM",DebertaForMaskedLM]],["deberta-v2",["DebertaV2ForMaskedLM",DebertaV2ForMaskedLM]],["mpnet",["MPNetForMaskedLM",MPNetForMaskedLM]],["albert",["AlbertForMaskedLM",AlbertForMaskedLM]],["distilbert",["DistilBertForMaskedLM",DistilBertForMaskedLM]],["roberta",["RobertaForMaskedLM",RobertaForMaskedLM]],["xlm",["XLMWithLMHeadModel",XLMWithLMHeadModel]],["xlm-roberta",["XLMRobertaForMaskedLM",XLMRobertaForMaskedLM]],["mobilebert",["MobileBertForMaskedLM",MobileBertForMaskedLM]],["squeezebert",["SqueezeBertForMaskedLM",SqueezeBertForMaskedLM]]]),MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES=new Map([["bert",["BertForQuestionAnswering",BertForQuestionAnswering]],["roformer",["RoFormerForQuestionAnswering",RoFormerForQuestionAnswering]],["electra",["ElectraForQuestionAnswering",ElectraForQuestionAnswering]],["convbert",["ConvBertForQuestionAnswering",ConvBertForQuestionAnswering]],["camembert",["CamembertForQuestionAnswering",CamembertForQuestionAnswering]],["deberta",["DebertaForQuestionAnswering",DebertaForQuestionAnswering]],["deberta-v2",["DebertaV2ForQuestionAnswering",DebertaV2ForQuestionAnswering]],["mpnet",["MPNetForQuestionAnswering",MPNetForQuestionAnswering]],["albert",["AlbertForQuestionAnswering",AlbertForQuestionAnswering]],["distilbert",["DistilBertForQuestionAnswering",DistilBertForQuestionAnswering]],["roberta",["RobertaForQuestionAnswering",RobertaForQuestionAnswering]],["xlm",["XLMForQuestionAnswering",XLMForQuestionAnswering]],["xlm-roberta",["XLMRobertaForQuestionAnswering",XLMRobertaForQuestionAnswering]],["mobilebert",["MobileBertForQuestionAnswering",MobileBertForQuestionAnswering]],["squeezebert",["SqueezeBertForQuestionAnswering",SqueezeBertForQuestionAnswering]]]),MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES=new Map([["vision-encoder-decoder",["VisionEncoderDecoderModel",VisionEncoderDecoderModel]]]),MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES=new Map([["vit",["ViTForImageClassification",ViTForImageClassification]],["mobilevit",["MobileViTForImageClassification",MobileViTForImageClassification]],["beit",["BeitForImageClassification",BeitForImageClassification]],["deit",["DeiTForImageClassification",DeiTForImageClassification]],["convnext",["ConvNextForImageClassification",ConvNextForImageClassification]],["convnextv2",["ConvNextV2ForImageClassification",ConvNextV2ForImageClassification]],["dinov2",["Dinov2ForImageClassification",Dinov2ForImageClassification]],["resnet",["ResNetForImageClassification",ResNetForImageClassification]],["swin",["SwinForImageClassification",SwinForImageClassification]],["segformer",["SegformerForImageClassification",SegformerForImageClassification]]]),MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES=new Map([["detr",["DetrForObjectDetection",DetrForObjectDetection]],["table-transformer",["TableTransformerForObjectDetection",TableTransformerForObjectDetection]],["yolos",["YolosForObjectDetection",YolosForObjectDetection]]]),MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES=new Map([["owlvit",["OwlViTForObjectDetection",OwlViTForObjectDetection]],["owlv2",["Owlv2ForObjectDetection",Owlv2ForObjectDetection]]]),MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES=new Map([["detr",["DetrForSegmentation",DetrForSegmentation]],["clipseg",["CLIPSegForImageSegmentation",CLIPSegForImageSegmentation]]]),MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES=new Map([["segformer",["SegformerForSemanticSegmentation",SegformerForSemanticSegmentation]]]),MODEL_FOR_MASK_GENERATION_MAPPING_NAMES=new Map([["sam",["SamModel",SamModel]]]),MODEL_FOR_CTC_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForCTC",Wav2Vec2ForCTC]],["wav2vec2-bert",["Wav2Vec2BertForCTC",Wav2Vec2BertForCTC]],["unispeech",["UniSpeechForCTC",UniSpeechForCTC]],["unispeech-sat",["UniSpeechSatForCTC",UniSpeechSatForCTC]],["wavlm",["WavLMForCTC",WavLMForCTC]],["hubert",["HubertForCTC",HubertForCTC]]]),MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES=new Map([["wav2vec2",["Wav2Vec2ForSequenceClassification",Wav2Vec2ForSequenceClassification]],["wav2vec2-bert",["Wav2Vec2BertForSequenceClassification",Wav2Vec2BertForSequenceClassification]],["unispeech",["UniSpeechForSequenceClassification",UniSpeechForSequenceClassification]],["unispeech-sat",["UniSpeechSatForSequenceClassification",UniSpeechSatForSequenceClassification]],["wavlm",["WavLMForSequenceClassification",WavLMForSequenceClassification]],["hubert",["HubertForSequenceClassification",HubertForSequenceClassification]],["audio-spectrogram-transformer",["ASTForAudioClassification",ASTForAudioClassification]]]),MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES=new Map([["wavlm",["WavLMForXVector",WavLMForXVector]]]),MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES=new Map([["unispeech-sat",["UniSpeechSatForAudioFrameClassification",UniSpeechSatForAudioFrameClassification]],["wavlm",["WavLMForAudioFrameClassification",WavLMForAudioFrameClassification]],["wav2vec2",["Wav2Vec2ForAudioFrameClassification",Wav2Vec2ForAudioFrameClassification]]]),MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES=new Map([["vitmatte",["VitMatteForImageMatting",VitMatteForImageMatting]]]),MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES=new Map([["swin2sr",["Swin2SRForImageSuperResolution",Swin2SRForImageSuperResolution]]]),MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES=new Map([["dpt",["DPTForDepthEstimation",DPTForDepthEstimation]],["depth_anything",["DepthAnythingForDepthEstimation",DepthAnythingForDepthEstimation]],["glpn",["GLPNForDepthEstimation",GLPNForDepthEstimation]]]),MODEL_CLASS_TYPE_MAPPING=[[MODEL_MAPPING_NAMES_ENCODER_ONLY,MODEL_TYPES.EncoderOnly],[MODEL_MAPPING_NAMES_ENCODER_DECODER,MODEL_TYPES.EncoderDecoder],[MODEL_MAPPING_NAMES_DECODER_ONLY,MODEL_TYPES.DecoderOnly],[MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,MODEL_TYPES.Seq2Seq],[MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES,MODEL_TYPES.Seq2Seq],[MODEL_WITH_LM_HEAD_MAPPING_NAMES,MODEL_TYPES.DecoderOnly],[MODEL_FOR_MASKED_LM_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES,MODEL_TYPES.Vision2Seq],[MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_MASK_GENERATION_MAPPING_NAMES,MODEL_TYPES.MaskGeneration],[MODEL_FOR_CTC_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES,MODEL_TYPES.Seq2Seq],[MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES,MODEL_TYPES.EncoderOnly],[MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES,MODEL_TYPES.EncoderOnly]];for(const[t,e]of MODEL_CLASS_TYPE_MAPPING)for(const[o,i]of t.values())MODEL_TYPE_MAPPING.set(o,e),MODEL_CLASS_TO_NAME_MAPPING.set(i,o),MODEL_NAME_TO_CLASS_MAPPING.set(o,i);const CUSTOM_MAPPING=[["CLIPTextModelWithProjection",CLIPTextModelWithProjection,MODEL_TYPES.EncoderOnly],["CLIPVisionModelWithProjection",CLIPVisionModelWithProjection,MODEL_TYPES.EncoderOnly],["SiglipTextModel",SiglipTextModel,MODEL_TYPES.EncoderOnly],["SiglipVisionModel",SiglipVisionModel,MODEL_TYPES.EncoderOnly],["ClapTextModelWithProjection",ClapTextModelWithProjection,MODEL_TYPES.EncoderOnly],["ClapAudioModelWithProjection",ClapAudioModelWithProjection,MODEL_TYPES.EncoderOnly]];for(const[t,e,o]of CUSTOM_MAPPING)MODEL_TYPE_MAPPING.set(t,o),MODEL_CLASS_TO_NAME_MAPPING.set(e,t),MODEL_NAME_TO_CLASS_MAPPING.set(t,e);class AutoModel extends PretrainedMixin{static MODEL_CLASS_MAPPINGS=MODEL_CLASS_TYPE_MAPPING.map(e=>e[0]);static BASE_IF_FAIL=!0}class Seq2SeqLMOutput extends ModelOutput{constructor({logits:e,past_key_values:o,encoder_outputs:i,decoder_attentions:u=null,cross_attentions:n=null}){super(),this.logits=e,this.past_key_values=o,this.encoder_outputs=i,this.decoder_attentions=u,this.cross_attentions=n}}class SequenceClassifierOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class XVectorOutput extends ModelOutput{constructor({logits:e,embeddings:o}){super(),this.logits=e,this.embeddings=o}}class TokenClassifierOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class MaskedLMOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class QuestionAnsweringModelOutput extends ModelOutput{constructor({start_logits:e,end_logits:o}){super(),this.start_logits=e,this.end_logits=o}}class CausalLMOutput extends ModelOutput{constructor({logits:e}){super(),this.logits=e}}class ImageMattingOutput extends ModelOutput{constructor({alphas:e}){super(),this.alphas=e}}class VitsModelOutput extends ModelOutput{constructor({waveform:e,spectrogram:o}){super(),this.waveform=e,this.spectrogram=o}}const BROWSER_ENV=typeof self<"u",WEBWORKER_ENV=BROWSER_ENV&&self.constructor.name==="DedicatedWorkerGlobalScope";let createCanvasFunction,ImageDataClass,loadImageFunction;if(BROWSER_ENV)createCanvasFunction=(t,e)=>{if(!self.OffscreenCanvas)throw new Error("OffscreenCanvas not supported by this browser.");return new self.OffscreenCanvas(t,e)},loadImageFunction=self.createImageBitmap,ImageDataClass=self.ImageData;else if(sharp)loadImageFunction=async t=>{const o=(await t.metadata()).channels,{data:i,info:u}=await t.raw().toBuffer({resolveWithObject:!0}),n=new RawImage(new Uint8ClampedArray(i),u.width,u.height,u.channels);return o!==void 0&&o!==u.channels&&n.convert(o),n};else throw new Error("Unable to load image processing library.");const RESAMPLING_MAPPING={0:"nearest",1:"lanczos",2:"bilinear",3:"bicubic",4:"box",5:"hamming"},CONTENT_TYPE_MAP=new Map([["png","image/png"],["jpg","image/jpeg"],["jpeg","image/jpeg"],["gif","image/gif"]]);class RawImage{constructor(e,o,i,u){this.data=e,this.width=o,this.height=i,this.channels=u}get size(){return[this.width,this.height]}static async read(e){if(e instanceof RawImage)return e;if(typeof e=="string"||e instanceof URL)return await this.fromURL(e);throw new Error(`Unsupported input type: ${typeof e}`)}static async fromURL(e){const o=await getFile(e);if(o.status!==200)throw new Error(`Unable to read image from "${e}" (${o.status} ${o.statusText})`);const i=await o.blob();return this.fromBlob(i)}static async fromBlob(e){if(BROWSER_ENV){const o=await loadImageFunction(e),i=createCanvasFunction(o.width,o.height).getContext("2d");return i.drawImage(o,0,0),new this(i.getImageData(0,0,o.width,o.height).data,o.width,o.height,4)}else{const o=sharp(await e.arrayBuffer());return await loadImageFunction(o)}}static fromTensor(e,o="CHW"){if(e.dims.length!==3)throw new Error(`Tensor should have 3 dimensions, but has ${e.dims.length} dimensions.`);if(o==="CHW")e=e.transpose(1,2,0);else if(o!=="HWC")throw new Error(`Unsupported channel format: ${o}`);if(!(e.data instanceof Uint8ClampedArray||e.data instanceof Uint8Array))throw new Error(`Unsupported tensor type: ${e.type}`);switch(e.dims[2]){case 1:case 2:case 3:case 4:return new RawImage(e.data,e.dims[1],e.dims[0],e.dims[2]);default:throw new Error(`Unsupported number of channels: ${e.dims[2]}`)}}grayscale(){if(this.channels===1)return this;const e=new Uint8ClampedArray(this.width*this.height*1);switch(this.channels){case 3:case 4:for(let o=0,i=0;o<this.data.length;o+=this.channels){const u=this.data[o],n=this.data[o+1],s=this.data[o+2];e[i++]=Math.round(.2989*u+.587*n+.114*s)}break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,1)}rgb(){if(this.channels===3)return this;const e=new Uint8ClampedArray(this.width*this.height*3);switch(this.channels){case 1:for(let o=0,i=0;o<this.data.length;++o)e[i++]=this.data[o],e[i++]=this.data[o],e[i++]=this.data[o];break;case 4:for(let o=0,i=0;o<this.data.length;o+=4)e[i++]=this.data[o],e[i++]=this.data[o+1],e[i++]=this.data[o+2];break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,3)}rgba(){if(this.channels===4)return this;const e=new Uint8ClampedArray(this.width*this.height*4);switch(this.channels){case 1:for(let o=0,i=0;o<this.data.length;++o)e[i++]=this.data[o],e[i++]=this.data[o],e[i++]=this.data[o],e[i++]=255;break;case 3:for(let o=0,i=0;o<this.data.length;o+=3)e[i++]=this.data[o],e[i++]=this.data[o+1],e[i++]=this.data[o+2],e[i++]=255;break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this._update(e,this.width,this.height,4)}async resize(e,o,{resample:i=2}={}){let u=RESAMPLING_MAPPING[i]??i;if(BROWSER_ENV){const n=this.channels,s=this.toCanvas(),d=createCanvasFunction(e,o).getContext("2d");return d.drawImage(s,0,0,e,o),new RawImage(d.getImageData(0,0,e,o).data,e,o,4).convert(n)}else{let n=this.toSharp();switch(u){case"box":case"hamming":(u==="box"||u==="hamming")&&(console.warn(`Resampling method ${u} is not yet supported. Using bilinear instead.`),u="bilinear");case"nearest":case"bilinear":case"bicubic":n=n.affine([e/this.width,0,0,o/this.height],{interpolator:u});break;case"lanczos":n=n.resize({width:e,height:o,fit:"fill",kernel:"lanczos3"});break;default:throw new Error(`Resampling method ${u} is not supported.`)}return await loadImageFunction(n)}}async pad([e,o,i,u]){if(e=Math.max(e,0),o=Math.max(o,0),i=Math.max(i,0),u=Math.max(u,0),e===0&&o===0&&i===0&&u===0)return this;if(BROWSER_ENV){const n=this.channels,s=this.toCanvas(),d=this.width+e+o,f=this.height+i+u,r=createCanvasFunction(d,f).getContext("2d");return r.drawImage(s,0,0,this.width,this.height,e,i,d,f),new RawImage(r.getImageData(0,0,d,f).data,d,f,4).convert(n)}else{const n=this.toSharp().extend({left:e,right:o,top:i,bottom:u});return await loadImageFunction(n)}}async crop([e,o,i,u]){if(e=Math.max(e,0),o=Math.max(o,0),i=Math.min(i,this.width-1),u=Math.min(u,this.height-1),e===0&&o===0&&i===this.width-1&&u===this.height-1)return this;const n=i-e+1,s=u-o+1;if(BROWSER_ENV){const d=this.channels,f=this.toCanvas(),r=createCanvasFunction(n,s).getContext("2d");return r.drawImage(f,e,o,n,s,0,0,n,s),new RawImage(r.getImageData(0,0,n,s).data,n,s,4).convert(d)}else{const d=this.toSharp().extract({left:e,top:o,width:n,height:s});return await loadImageFunction(d)}}async center_crop(e,o){if(this.width===e&&this.height===o)return this;const i=(this.width-e)/2,u=(this.height-o)/2;if(BROWSER_ENV){const n=this.channels,s=this.toCanvas(),d=createCanvasFunction(e,o).getContext("2d");let f=0,r=0,a=0,l=0;return i>=0?f=i:a=-i,u>=0?r=u:l=-u,d.drawImage(s,f,r,e,o,a,l,e,o),new RawImage(d.getImageData(0,0,e,o).data,e,o,4).convert(n)}else{let n=this.toSharp();if(i>=0&&u>=0)n=n.extract({left:Math.floor(i),top:Math.floor(u),width:e,height:o});else if(i<=0&&u<=0){const s=Math.floor(-u),d=Math.floor(-i);n=n.extend({top:s,left:d,right:e-this.width-d,bottom:o-this.height-s})}else{let s=[0,0],d=0;u<0?(s[0]=Math.floor(-u),s[1]=o-this.height-s[0]):d=Math.floor(u);let f=[0,0],r=0;i<0?(f[0]=Math.floor(-i),f[1]=e-this.width-f[0]):r=Math.floor(i),n=n.extend({top:s[0],bottom:s[1],left:f[0],right:f[1]}).extract({left:r,top:d,width:e,height:o})}return await loadImageFunction(n)}}async toBlob(e="image/png",o=1){if(!BROWSER_ENV)throw new Error("toBlob() is only supported in browser environments.");return await this.toCanvas().convertToBlob({type:e,quality:o})}toCanvas(){if(!BROWSER_ENV)throw new Error("toCanvas() is only supported in browser environments.");const e=this.clone().rgba(),o=createCanvasFunction(e.width,e.height),i=new ImageDataClass(e.data,e.width,e.height);return o.getContext("2d").putImageData(i,0,0),o}_update(e,o,i,u=null){return this.data=e,this.width=o,this.height=i,u!==null&&(this.channels=u),this}clone(){return new RawImage(this.data.slice(),this.width,this.height,this.channels)}convert(e){if(this.channels===e)return this;switch(e){case 1:this.grayscale();break;case 3:this.rgb();break;case 4:this.rgba();break;default:throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)}return this}async save(e){if(BROWSER_ENV){if(WEBWORKER_ENV)throw new Error("Unable to save an image from a Web Worker.");const o=e.split(".").pop().toLowerCase(),i=CONTENT_TYPE_MAP.get(o)??"image/png",u=await this.toBlob(i),n=URL.createObjectURL(u),s=document.createElement("a");s.href=n,s.download=e,s.click(),s.remove()}else{if(env.useFS)return await this.toSharp().toFile(e);throw new Error("Unable to save the image because filesystem is disabled in this environment.")}}toSharp(){if(BROWSER_ENV)throw new Error("toSharp() is only supported in server-side environments.");return sharp(this.data,{raw:{width:this.width,height:this.height,channels:this.channels}})}}function hanning(t){if(t<1)return new Float64Array;if(t===1)return new Float64Array([1]);const e=t-1,o=Math.PI/e,i=new Float64Array(t);for(let u=0;u<t;++u){const n=2*u-e;i[u]=.5+.5*Math.cos(o*n)}return i}const HERTZ_TO_MEL_MAPPING={htk:t=>2595*Math.log10(1+t/700),kaldi:t=>1127*Math.log(1+t/700),slaney:(t,e=1e3,o=15,i=27/Math.log(6.4))=>t>=e?o+Math.log(t/e)*i:3*t/200};function hertz_to_mel(t,e="htk"){const o=HERTZ_TO_MEL_MAPPING[e];if(!o)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?o(t):t.map(i=>o(i))}const MEL_TO_HERTZ_MAPPING={htk:t=>700*(10**(t/2595)-1),kaldi:t=>700*(Math.exp(t/1127)-1),slaney:(t,e=1e3,o=15,i=Math.log(6.4)/27)=>t>=o?e*Math.exp(i*(t-o)):200*t/3};function mel_to_hertz(t,e="htk"){const o=MEL_TO_HERTZ_MAPPING[e];if(!o)throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');return typeof t=="number"?o(t):t.map(i=>o(i))}function _create_triangular_filter_bank(t,e){const o=Float64Array.from({length:e.length-1},(s,d)=>e[d+1]-e[d]),i=Array.from({length:t.length},()=>new Array(e.length));for(let s=0;s<t.length;++s){const d=i[s];for(let f=0;f<e.length;++f)d[f]=e[f]-t[s]}const u=e.length-2,n=Array.from({length:u},()=>new Array(t.length));for(let s=0;s<t.length;++s){const d=i[s];for(let f=0;f<u;++f){const r=-d[f]/o[f],a=d[f+2]/o[f+1];n[f][s]=Math.max(0,Math.min(r,a))}}return n}function linspace(t,e,o){const i=(e-t)/(o-1);return Float64Array.from({length:o},(u,n)=>t+i*n)}function mel_filter_bank(t,e,o,i,u,n=null,s="htk",d=!1){if(n!==null&&n!=="slaney")throw new Error('norm must be one of null or "slaney"');const f=hertz_to_mel(o,s),r=hertz_to_mel(i,s),a=linspace(f,r,e+2);let l=mel_to_hertz(a,s),p;if(d){const y=u/(t*2);p=hertz_to_mel(Float64Array.from({length:t},(T,v)=>v*y),s),l=a}else p=linspace(0,Math.floor(u/2),t);const m=_create_triangular_filter_bank(p,l);if(n!==null&&n==="slaney")for(let y=0;y<e;++y){const T=m[y],v=2/(l[y+2]-l[y]);for(let S=0;S<t;++S)T[S]*=v}return m}function padReflect(t,e,o){const i=new t.constructor(t.length+e+o),u=t.length-1;for(let n=0;n<t.length;++n)i[e+n]=t[n];for(let n=1;n<=e;++n)i[e-n]=t[calculateReflectOffset(n,u)];for(let n=1;n<=o;++n)i[u+e+n]=t[calculateReflectOffset(u-n,u)];return i}function _db_conversion_helper(t,e,o,i,u){if(o<=0)throw new Error("reference must be greater than zero");if(i<=0)throw new Error("min_value must be greater than zero");o=Math.max(i,o);const n=Math.log10(o);for(let s=0;s<t.length;++s)t[s]=e*Math.log10(Math.max(i,t[s])-n);if(u!==null){if(u<=0)throw new Error("db_range must be greater than zero");const s=max(t)[0]-u;for(let d=0;d<t.length;++d)t[d]=Math.max(t[d],s)}return t}function amplitude_to_db(t,e=1,o=1e-5,i=null){return _db_conversion_helper(t,20,e,o,i)}function power_to_db(t,e=1,o=1e-10,i=null){return _db_conversion_helper(t,10,e,o,i)}function spectrogram(t,e,o,i,{fft_length:u=null,power:n=1,center:s=!0,pad_mode:d="reflect",onesided:f=!0,preemphasis:r=null,mel_filters:a=null,mel_floor:l=1e-10,log_mel:p=null,reference:m=1,min_value:y=1e-10,db_range:T=null,remove_dc_offset:v=null,max_num_frames:S=null,do_pad:E=!0,transpose:A=!1}={}){const F=e.length;if(u===null&&(u=o),o>u)throw Error(`frame_length (${o}) may not be larger than fft_length (${u})`);if(F!==o)throw new Error(`Length of the window (${F}) must equal frame_length (${o})`);if(i<=0)throw new Error("hop_length must be greater than zero");if(s){if(d!=="reflect")throw new Error(`pad_mode="${d}" not implemented yet.`);const he=Math.floor((u-1)/2)+1;t=padReflect(t,he,he)}const B=Math.floor(1+Math.floor((t.length-o)/i)),J=f?Math.floor(u/2)+1:u;let Q=B,re=B;S!==null&&(S>B?E&&(re=S):re=Q=S);const V=new FFT(u),de=new Float64Array(u),ke=new Float64Array(V.outputBufferSize),Ne=new Array(Q);for(let he=0;he<Q;++he){const ue=he*i;for(let X=0;X<o;++X)de[X]=t[ue+X];if(v){let X=0;for(let we=0;we<o;++we)X+=de[we];const ve=X/o;for(let we=0;we<o;++we)de[we]-=ve}if(r!==null){for(let X=o-1;X>=1;--X)de[X]-=r*de[X-1];de[0]*=1-r}for(let X=0;X<e.length;++X)de[X]*=e[X];V.realTransform(ke,de);const pe=new Array(J);for(let X=0;X<pe.length;++X){const ve=X<<1;pe[X]=ke[ve]**2+ke[ve+1]**2}Ne[he]=pe}if(n!==null&&n!==2){const he=2/n;for(let ue=0;ue<Ne.length;++ue){const pe=Ne[ue];for(let X=0;X<pe.length;++X)pe[X]**=he}}const Me=a.length,Y=new Float32Array(Me*re),_e=A?[re,Me]:[Me,re];for(let he=0;he<Me;++he){const ue=a[he];for(let pe=0;pe<Q;++pe){const X=Ne[pe];let ve=0;for(let we=0;we<J;++we)ve+=ue[we]*X[we];Y[A?pe*Me+he:he*Q+pe]=Math.max(l,ve)}}if(n!==null&&p!==null){const he=Math.min(Y.length,Q*Me);switch(p){case"log":for(let ue=0;ue<he;++ue)Y[ue]=Math.log(Y[ue]);break;case"log10":for(let ue=0;ue<he;++ue)Y[ue]=Math.log10(Y[ue]);break;case"dB":if(n===1)amplitude_to_db(Y,m,y,T);else if(n===2)power_to_db(Y,m,y,T);else throw new Error(`Cannot use log_mel option '${p}' with power ${n}`);break;default:throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${p}'`)}}return{data:Y,dims:_e}}function window_function(t,e,{periodic:o=!0,frame_length:i=null,center:u=!0}={}){const n=o?t+1:t;let s;switch(e){case"boxcar":s=new Float64Array(n).fill(1);break;case"hann":case"hann_window":s=hanning(n);break;case"povey":s=hanning(n).map(d=>Math.pow(d,.85));break;default:throw new Error(`Unknown window type ${e}.`)}if(o&&(s=s.subarray(0,t)),i===null)return s;if(t>i)throw new Error(`Length of the window (${t}) may not be larger than frame_length (${i})`);return s}function center_to_corners_format([t,e,o,i]){return[t-o/2,e-i/2,t+o/2,e+i/2]}function post_process_object_detection(t,e=.5,o=null,i=!1){const u=t.logits,n=t.pred_boxes,[s,d,f]=u.dims;if(o!==null&&o.length!==s)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let r=[];for(let a=0;a<s;++a){let l=o!==null?o[a]:null,p={boxes:[],classes:[],scores:[]},m=u[a],y=n[a];for(let T=0;T<d;++T){let v=m[T],S=[],E;if(i){E=v.sigmoid().data;for(let A=0;A<E.length;++A)E[A]>e&&S.push(A)}else{let A=max(v.data)[1];if(A===f-1)continue;S.push(A),E=softmax(v.data)}for(const A of S){let F=y[T].data;F=center_to_corners_format(F),l!==null&&(F=F.map((B,J)=>B*l[(J+1)%2])),p.boxes.push(F),p.classes.push(A),p.scores.push(E[A])}}r.push(p)}return r}function validate_audio_inputs(t,e){if(!(t instanceof Float32Array||t instanceof Float64Array))throw new Error(`${e} expects input to be a Float32Array or a Float64Array, but got ${t?.constructor?.name??typeof t} instead. If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)}function constraint_to_multiple_of(t,e,o=0,i=null){let u=Math.round(t/e)*e;return i!==null&&u>i&&(u=Math.floor(t/e)*e),u<o&&(u=Math.ceil(t/e)*e),u}function enforce_size_divisibility([t,e],o){return[Math.floor(t/o)*o,Math.floor(e/o)*o]}class FeatureExtractor extends Callable{constructor(e){super(),this.config=e}}class ImageFeatureExtractor extends FeatureExtractor{constructor(e){super(e),this.image_mean=this.config.image_mean??this.config.mean,this.image_std=this.config.image_std??this.config.std,this.resample=this.config.resample??2,this.do_rescale=this.config.do_rescale??!0,this.rescale_factor=this.config.rescale_factor??1/255,this.do_normalize=this.config.do_normalize,this.do_resize=this.config.do_resize,this.do_thumbnail=this.config.do_thumbnail,this.size=this.config.size,this.size_divisibility=this.config.size_divisibility??this.config.size_divisor,this.do_center_crop=this.config.do_center_crop,this.crop_size=this.config.crop_size,this.do_convert_rgb=this.config.do_convert_rgb??!0,this.do_crop_margin=this.config.do_crop_margin,this.pad_size=this.config.pad_size,this.do_pad=this.config.do_pad,this.do_pad&&!this.pad_size&&this.size&&this.size.width!==void 0&&this.size.height!==void 0&&(this.pad_size=this.size)}async thumbnail(e,o,i=2){const u=e.height,n=e.width,s=o.height,d=o.width;let f=Math.min(u,s),r=Math.min(n,d);return f===u&&r===n?e:(u>n?r=Math.floor(n*f/u):n>u&&(f=Math.floor(u*r/n)),await e.resize(r,f,{resample:i}))}async crop_margin(e,o=200){const i=e.clone().grayscale(),u=min(i.data)[0],s=max(i.data)[0]-u;if(s===0)return e;const d=o/255;let f=i.width,r=i.height,a=0,l=0;for(let p=0;p<i.height;++p){const m=p*i.width;for(let y=0;y<i.width;++y)(i.data[m+y]-u)/s<d&&(f=Math.min(f,y),r=Math.min(r,p),a=Math.max(a,y),l=Math.max(l,p))}return e=await e.crop([f,r,a,l]),e}pad_image(e,o,i,{mode:u="constant",center:n=!1,constant_values:s=0}={}){const[d,f,r]=o;let a,l;if(typeof i=="number"?(a=i,l=i):(a=i.width,l=i.height),a!==d||l!==f){const p=new Float32Array(a*l*r);if(Array.isArray(s))for(let T=0;T<p.length;++T)p[T]=s[T%r];else s!==0&&p.fill(s);const[m,y]=n?[Math.floor((a-d)/2),Math.floor((l-f)/2)]:[0,0];for(let T=0;T<f;++T){const v=(T+y)*a,S=T*d;for(let E=0;E<d;++E){const A=(v+E+m)*r,F=(S+E)*r;for(let B=0;B<r;++B)p[A+B]=e[F+B]}}if(u==="symmetric"){if(n)throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");const T=f-1,v=d-1;for(let S=0;S<l;++S){const E=S*a,A=calculateReflectOffset(S,T)*d;for(let F=0;F<a;++F){if(S<f&&F<d)continue;const B=(E+F)*r,J=(A+calculateReflectOffset(F,v))*r;for(let Q=0;Q<r;++Q)p[B+Q]=e[J+Q]}}}e=p,o=[l,a,r]}return[e,o]}rescale(e){for(let o=0;o<e.length;++o)e[o]=this.rescale_factor*e[o]}get_resize_output_image_size(e,o){const[i,u]=e.size;let n,s;if(this.do_thumbnail){const{height:d,width:f}=o;n=Math.min(d,f)}else Number.isInteger(o)?(n=o,s=this.config.max_size??n):o!==void 0&&(n=o.shortest_edge,s=o.longest_edge);if(n!==void 0||s!==void 0){const d=n===void 0?1:Math.max(n/i,n/u),f=i*d,r=u*d,a=s===void 0?1:Math.min(s/f,s/r);let l=Math.floor(Number((f*a).toFixed(2))),p=Math.floor(Number((r*a).toFixed(2)));return this.size_divisibility!==void 0&&([l,p]=enforce_size_divisibility([l,p],this.size_divisibility)),[l,p]}else if(o!==void 0&&o.width!==void 0&&o.height!==void 0){let d=o.width,f=o.height;if(this.config.keep_aspect_ratio&&this.config.ensure_multiple_of){let r=o.height/u,a=o.width/i;Math.abs(1-a)<Math.abs(1-r)?r=a:a=r,f=constraint_to_multiple_of(r*u,this.config.ensure_multiple_of),d=constraint_to_multiple_of(a*i,this.config.ensure_multiple_of)}return[d,f]}else{if(this.size_divisibility!==void 0)return enforce_size_divisibility([i,u],this.size_divisibility);throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(o)}`)}}async resize(e){const[o,i]=this.get_resize_output_image_size(e,this.size);return await e.resize(o,i,{resample:this.resample})}async preprocess(e,{do_normalize:o=null,do_pad:i=null,do_convert_rgb:u=null,do_convert_grayscale:n=null}={}){this.do_crop_margin&&(e=await this.crop_margin(e));const[s,d]=e.size;if(u??this.do_convert_rgb?e=e.rgb():n&&(e=e.grayscale()),this.do_resize&&(e=await this.resize(e)),this.do_thumbnail&&(e=await this.thumbnail(e,this.size,this.resample)),this.do_center_crop){let m,y;Number.isInteger(this.crop_size)?(m=this.crop_size,y=this.crop_size):(m=this.crop_size.width,y=this.crop_size.height),e=await e.center_crop(m,y)}const f=[e.height,e.width];let r=Float32Array.from(e.data),a=[e.height,e.width,e.channels];if(this.do_rescale&&this.rescale(r),o??this.do_normalize){let m=this.image_mean;Array.isArray(this.image_mean)||(m=new Array(e.channels).fill(m));let y=this.image_std;if(Array.isArray(this.image_std)||(y=new Array(e.channels).fill(m)),m.length!==e.channels||y.length!==e.channels)throw new Error(`When set to arrays, the length of \`image_mean\` (${m.length}) and \`image_std\` (${y.length}) must match the number of channels in the image (${e.channels}).`);for(let T=0;T<r.length;T+=e.channels)for(let v=0;v<e.channels;++v)r[T+v]=(r[T+v]-this.image_mean[v])/this.image_std[v]}if(this.do_pad&&this.pad_size){const m=new Float32Array(this.pad_size.width*this.pad_size.height*e.channels);for(let y=0;y<e.height;++y){const T=y*this.pad_size.width,v=y*e.width;for(let S=0;S<e.width;++S){const E=(T+S)*e.channels,A=(v+S)*e.channels;for(let F=0;F<e.channels;++F)m[E+F]=r[A+F]}}r=m,a=[this.pad_size.height,this.pad_size.width,e.channels]}const l=new Tensor("float32",r,a),p=transpose(l,[2,0,1]);return{original_size:[d,s],reshaped_input_size:f,pixel_values:p}}async _call(e,...o){Array.isArray(e)||(e=[e]);const i=await Promise.all(e.map(n=>this.preprocess(n)));return{pixel_values:stack(i.map(n=>n.pixel_values),0),original_sizes:i.map(n=>n.original_size),reshaped_input_sizes:i.map(n=>n.reshaped_input_size)}}}class SegformerFeatureExtractor extends ImageFeatureExtractor{post_process_semantic_segmentation(e,o=null){const i=e.logits,u=i.dims[0];if(o!==null&&o.length!==u)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");const n=[];for(let s=0;s<u;++s){const d=o!==null?o[s]:null;let f=i[s];d!==null&&(f=interpolate(f,d,"bilinear",!1));const[r,a]=d??f.dims.slice(-2),l=new Tensor("int32",new Int32Array(r*a),[r,a]),p=f[0].data;for(let v=1;v<f.dims[0];++v){const S=f[v].data;for(let E=0;E<S.length;++E)S[E]>p[E]&&(p[E]=S[E],l.data[E]=v)}const m=new Array(f.dims[0]),y=l.data;for(let v=0;v<y.length;++v){const S=y[v];m[S]=S}const T=m.filter(v=>v!==void 0);n.push({segmentation:l,labels:T})}return n}}class DPTImageProcessor extends ImageFeatureExtractor{}class BitImageProcessor extends ImageFeatureExtractor{}class DPTFeatureExtractor extends ImageFeatureExtractor{}class GLPNFeatureExtractor extends ImageFeatureExtractor{}class CLIPFeatureExtractor extends ImageFeatureExtractor{}class ChineseCLIPFeatureExtractor extends ImageFeatureExtractor{}class SiglipImageProcessor extends ImageFeatureExtractor{}class ConvNextFeatureExtractor extends ImageFeatureExtractor{constructor(e){super(e),this.crop_pct=this.config.crop_pct??224/256}async resize(e){const o=this.size?.shortest_edge;if(o===void 0)throw new Error("Size dictionary must contain 'shortest_edge' key.");if(o<384){const i=Math.floor(o/this.crop_pct),[u,n]=this.get_resize_output_image_size(e,{shortest_edge:i});e=await e.resize(u,n,{resample:this.resample}),e=await e.center_crop(o,o)}else e=await e.resize(o,o,{resample:this.resample});return e}}class ConvNextImageProcessor extends ConvNextFeatureExtractor{}class ViTFeatureExtractor extends ImageFeatureExtractor{}class ViTImageProcessor extends ImageFeatureExtractor{}class MobileViTFeatureExtractor extends ImageFeatureExtractor{}class OwlViTFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...e){return post_process_object_detection(...e)}}class Owlv2ImageProcessor extends OwlViTFeatureExtractor{}class DeiTFeatureExtractor extends ImageFeatureExtractor{}class BeitFeatureExtractor extends ImageFeatureExtractor{}class DonutFeatureExtractor extends ImageFeatureExtractor{pad_image(e,o,i,u={}){const[n,s,d]=o;let f=this.image_mean;Array.isArray(this.image_mean)||(f=new Array(d).fill(f));let r=this.image_std;Array.isArray(r)||(r=new Array(d).fill(f));const a=f.map((l,p)=>-l/this.image_std[p]);return super.pad_image(e,o,i,{center:!0,constant_values:a,...u})}}class NougatImageProcessor extends DonutFeatureExtractor{}class DetrFeatureExtractor extends ImageFeatureExtractor{async _call(e){const o=await super._call(e),i=[o.pixel_values.dims[0],64,64],u=new Tensor("int64",new BigInt64Array(i.reduce((n,s)=>n*s)).fill(1n),i);return{...o,pixel_mask:u}}post_process_object_detection(...e){return post_process_object_detection(...e)}remove_low_and_no_objects(e,o,i,u){let n=[],s=[],d=[];for(let f=0;f<e.dims[0];++f){let r=e[f],a=o[f],l=max(r.data)[1];if(l===u)continue;let m=softmax(r.data)[l];m>i&&(n.push(a),s.push(m),d.push(l))}return[n,s,d]}check_segment_validity(e,o,i,u=.5,n=.8){let s=[],d=0,f=0;for(let a=0;a<e.length;++a)e[a]===i&&(s.push(a),++d),o[i].data[a]>=u&&++f;let r=d>0&&f>0;return r&&(r=d/f>n),[r,s]}compute_segments(e,o,i,u,n,s=null,d=null){let[f,r]=d??e[0].dims,a=new Tensor("int32",new Int32Array(f*r),[f,r]),l=[];if(d!==null)for(let T=0;T<e.length;++T)e[T]=interpolate(e[T],d,"bilinear",!1);let p=new Int32Array(e[0].data.length),m=new Float32Array(e[0].data.length);for(let T=0;T<e.length;++T){let v=o[T];for(let S=0;S<e[T].data.length;++S)e[T].data[S]*=v,e[T].data[S]>m[S]&&(p[S]=T,m[S]=e[T].data[S])}let y=0;for(let T=0;T<i.length;++T){let v=i[T],[S,E]=this.check_segment_validity(p,e,T,u,n);if(S){++y;for(let A of E)a.data[A]=y;l.push({id:y,label_id:v,score:o[T]})}}return[a,l]}post_process_panoptic_segmentation(e,o=.5,i=.5,u=.8,n=null,s=null){n===null&&(console.warn("`label_ids_to_fuse` unset. No instance will be fused."),n=new Set);const d=e.logits,r=e.pred_masks.sigmoid();let[a,l,p]=d.dims;if(p-=1,s!==null&&s.length!==a)throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");let m=[];for(let y=0;y<a;++y){let T=s!==null?s[y]:null,v=d[y],S=r[y],[E,A,F]=this.remove_low_and_no_objects(v,S,o,p);if(F.length===0){let[Q,re]=T??S.dims.slice(-2),V=new Tensor("int32",new Int32Array(Q*re).fill(-1),[Q,re]);m.push({segmentation:V,segments_info:[]});continue}let[B,J]=this.compute_segments(E,A,F,i,u,n,T);m.push({segmentation:B,segments_info:J})}return m}post_process_instance_segmentation(){throw Error("Not implemented yet")}}class YolosFeatureExtractor extends ImageFeatureExtractor{post_process_object_detection(...e){return post_process_object_detection(...e)}}class SamImageProcessor extends ImageFeatureExtractor{reshape_input_points(e,o,i){e=structuredClone(e);let u=calculateDimensions(e);if(u.length===3)u=[1,...u],e=[e];else if(u.length!==4)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");for(let n=0;n<e.length;++n){let s=o[n],d=i[n],f=[d[0]/s[0],d[1]/s[1]];for(let r=0;r<e[n].length;++r)for(let a=0;a<e[n][r].length;++a)for(let l=0;l<e[n][r][a].length;++l)e[n][r][a][l]*=f[l]}return new Tensor("float32",Float32Array.from(e.flat(1/0)),u)}add_input_labels(e,o){let i=calculateDimensions(e);if(i.length===2)i=[1,...i],e=[e];else if(i.length!==3)throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");if(i.some((u,n)=>u!==o.dims[n]))throw Error(`The first ${i.length} dimensions of 'input_points' and 'input_labels' must be the same.`);return new Tensor("int64",e.flat(1/0).map(BigInt),i)}async _call(e,o=null,i=null){const u=await super._call(e);if(o&&(u.input_points=this.reshape_input_points(o,u.original_sizes,u.reshaped_input_sizes)),i){if(!u.input_points)throw Error("`input_points` must be provided if `input_labels` are provided.");u.input_labels=this.add_input_labels(i,u.input_points)}return u}post_process_masks(e,o,i,{mask_threshold:u=0,binarize:n=!0,pad_size:s=null}={}){const d=[];s=s??this.pad_size;const f=[s.height,s.width];for(let r=0;r<o.length;++r){const a=o[r],l=i[r],p=e[r],m=[];for(let y=0;y<p.dims[0];++y){const T=p[y];let v=interpolate(T,f,"bilinear",!1);if(v=v.slice(null,[0,l[0]],[0,l[1]]),v=interpolate(v,a,"bilinear",!1),n){const S=new Uint8Array(v.data.length);for(let E=0;E<v.data.length;++E)v.data[E]>u&&(S[E]=1);v=new Tensor("bool",S,v.dims)}m.push(v)}d.push(stack(m))}return d}generate_crop_boxes(e,o,{crop_n_layers:i=0,overlap_ratio:u=512/1500,points_per_crop:n=32,crop_n_points_downscale_factor:s=1}={}){}}class Swin2SRImageProcessor extends ImageFeatureExtractor{pad_image(e,o,i,u={}){const[n,s,d]=o;return super.pad_image(e,o,{width:n+(i-n%i)%i,height:s+(i-s%i)%i},{mode:"symmetric",center:!1,constant_values:-1,...u})}}class VitMatteImageProcessor extends ImageFeatureExtractor{async _call(e,o){Array.isArray(e)||(e=[e]),Array.isArray(o)||(o=[o]);const i=await Promise.all(e.map(s=>this.preprocess(s))),u=await Promise.all(o.map(s=>this.preprocess(s,{do_normalize:!1,do_convert_rgb:!1,do_convert_grayscale:!0})));return{pixel_values:stack(i.map((s,d)=>cat([s.pixel_values,u[d].pixel_values],0)),0),original_sizes:i.map(s=>s.original_size),reshaped_input_sizes:i.map(s=>s.reshaped_input_size)}}}class WhisperFeatureExtractor extends FeatureExtractor{constructor(e){super(e),this.config.mel_filters??=mel_filter_bank(Math.floor(1+this.config.n_fft/2),this.config.feature_size,0,8e3,this.config.sampling_rate,"slaney","slaney"),this.window=window_function(this.config.n_fft,"hann")}_extract_fbank_features(e){const{data:o,dims:i}=spectrogram(e,this.window,this.config.n_fft,this.config.hop_length,{power:2,mel_filters:this.config.mel_filters,log_mel:"log10",max_num_frames:this.config.nb_max_frames}),u=max(o)[0];for(let n=0;n<o.length;++n)o[n]=(Math.max(o[n],u-8)+4)/4;return{data:o,dims:i}}async _call(e){validate_audio_inputs(e,"WhisperFeatureExtractor");let o;e.length>this.config.n_samples?(console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."),o=e.slice(0,this.config.n_samples)):(o=new Float32Array(this.config.n_samples),o.set(e));const{data:i,dims:u}=this._extract_fbank_features(o);return{input_features:new Tensor("float32",i,[1,...u])}}}class Wav2Vec2FeatureExtractor extends FeatureExtractor{_zero_mean_unit_var_norm(e){const i=e.reduce((n,s)=>n+s,0)/e.length,u=e.reduce((n,s)=>n+(s-i)**2,0)/e.length;return e.map(n=>(n-i)/Math.sqrt(u+1e-7))}async _call(e){validate_audio_inputs(e,"Wav2Vec2FeatureExtractor"),e instanceof Float64Array&&(e=new Float32Array(e));let o=e;this.config.do_normalize&&(o=this._zero_mean_unit_var_norm(o));const i=[1,o.length];return{input_values:new Tensor("float32",o,i),attention_mask:new Tensor("int64",new BigInt64Array(o.length).fill(1n),i)}}}class SeamlessM4TFeatureExtractor extends FeatureExtractor{constructor(e){super(e);const o=this.config.sampling_rate,i=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(o/2),o,null,"kaldi",!0);for(let u=0;u<i.length;++u)i[u].push(0);this.mel_filters=i,this.window=window_function(400,"povey",{periodic:!1})}_extract_fbank_features(e,o){return e=e.map(i=>i*32768),spectrogram(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:o,transpose:!0})}async _call(e,{padding:o=!0,pad_to_multiple_of:i=2,do_normalize_per_mel_bins:u=!0,return_attention_mask:n=!0}={}){validate_audio_inputs(e,"SeamlessM4TFeatureExtractor");let s=this._extract_fbank_features(e,this.config.max_length);if(u){const[y,T]=s.dims;for(let v=0;v<T;++v){let S=0;for(let B=0;B<y;++B)S+=s.data[B*T+v];const E=S/y;let A=0;for(let B=0;B<y;++B)A+=(s.data[B*T+v]-E)**2;A/=y-1;const F=Math.sqrt(A+1e-7);for(let B=0;B<y;++B){const J=B*T+v;s.data[J]=(s.data[J]-E)/F}}}let d;if(o){const[y,T]=s.dims,v=y%i;if(v>0){const S=new Float32Array(T*(y+v));S.set(s.data),S.fill(this.config.padding_value,s.data.length);const E=y+v;s={data:S,dims:[E,T]},n&&(d=new Tensor("int64",new BigInt64Array(E),[1,E]),d.data.fill(1n,0,y))}}const[f,r]=s.dims,a=this.config.stride;if(f%a!==0)throw new Error(`The number of frames (${f}) must be a multiple of the stride (${a}).`);const p=new Tensor("float32",s.data,s.dims).view(1,Math.floor(f/a),r*a),m={input_features:p};if(n){const y=p.dims[1],T=new Tensor("int64",new BigInt64Array(y),[1,y]);if(d)for(let v=1,S=0;v<f;v+=a,++S)T.data[S]=d.data[v];else T.data.fill(1n);m.attention_mask=T}return m}}class ASTFeatureExtractor extends FeatureExtractor{constructor(e){super(e);const o=this.config.sampling_rate,i=mel_filter_bank(256,this.config.num_mel_bins,20,Math.floor(o/2),o,null,"kaldi",!0);for(let u=0;u<i.length;++u)i[u].push(0);this.mel_filters=i,this.window=window_function(400,"hann",{periodic:!1}),this.mean=this.config.mean,this.std=this.config.std}_extract_fbank_features(e,o){return spectrogram(e,this.window,400,160,{fft_length:512,power:2,center:!1,preemphasis:.97,mel_filters:this.mel_filters,log_mel:"log",mel_floor:1192092955078125e-22,remove_dc_offset:!0,max_num_frames:o,transpose:!0})}async _call(e){validate_audio_inputs(e,"ASTFeatureExtractor");const o=this._extract_fbank_features(e,this.config.max_length);if(this.config.do_normalize){const i=this.std*2;for(let u=0;u<o.data.length;++u)o.data[u]=(o.data[u]-this.mean)/i}return{input_values:new Tensor("float32",o.data,[1,...o.dims])}}}class ClapFeatureExtractor extends FeatureExtractor{constructor(e){super(e),this.mel_filters=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,null,"htk"),this.mel_filters_slaney=mel_filter_bank(this.config.nb_frequency_bins,this.config.feature_size,this.config.frequency_min,this.config.frequency_max,this.config.sampling_rate,"slaney","slaney"),this.window=window_function(this.config.fft_window_size,"hann")}_get_input_mel(e,o,i,u){let n,s=!1;const d=e.length-o;if(d>0)if(i==="rand_trunc"){s=!0;const f=Math.floor(Math.random()*(d+1));e=e.subarray(f,f+o),n=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),n.dims=[1,...n.dims]}else throw new Error(`Truncation strategy "${i}" not implemented`);else{if(d<0){let f=new Float64Array(o);if(f.set(e),u==="repeat")for(let r=e.length;r<o;r+=e.length)f.set(e.subarray(0,Math.min(e.length,o-r)),r);else if(u==="repeatpad")for(let r=e.length;r<-d;r+=e.length)f.set(e,r);e=f}if(i==="fusion")throw new Error(`Truncation strategy "${i}" not implemented`);n=this._extract_fbank_features(e,this.mel_filters_slaney,this.config.nb_max_samples),n.dims=[1,...n.dims]}return{...n,longer:s}}_extract_fbank_features(e,o,i=null){return spectrogram(e,this.window,this.config.fft_window_size,this.config.hop_length,{power:2,mel_filters:o,log_mel:"dB",max_num_frames:i,do_pad:!1,transpose:!0})}async _call(e,{max_length:o=null}={}){validate_audio_inputs(e,"ClapFeatureExtractor");const i=this._get_input_mel(e,o??this.config.nb_max_samples,this.config.truncation,this.config.padding);return{input_features:new Tensor("float32",i.data,[1,...i.dims])}}}class SpeechT5FeatureExtractor extends FeatureExtractor{}class Processor extends Callable{constructor(e){super(),this.feature_extractor=e}async _call(e,...o){return await this.feature_extractor(e,...o)}}class SamProcessor extends Processor{async _call(...e){return await this.feature_extractor(...e)}post_process_masks(...e){return this.feature_extractor.post_process_masks(...e)}reshape_input_points(...e){return this.feature_extractor.reshape_input_points(...e)}}class WhisperProcessor extends Processor{async _call(e){return await this.feature_extractor(e)}}class Wav2Vec2ProcessorWithLM extends Processor{async _call(e){return await this.feature_extractor(e)}}class SpeechT5Processor extends Processor{async _call(e){return await this.feature_extractor(e)}}class OwlViTProcessor extends Processor{}class AutoProcessor{static FEATURE_EXTRACTOR_CLASS_MAPPING={ImageFeatureExtractor,WhisperFeatureExtractor,ViTFeatureExtractor,MobileViTFeatureExtractor,OwlViTFeatureExtractor,Owlv2ImageProcessor,CLIPFeatureExtractor,ChineseCLIPFeatureExtractor,SiglipImageProcessor,ConvNextFeatureExtractor,ConvNextImageProcessor,SegformerFeatureExtractor,BitImageProcessor,DPTImageProcessor,DPTFeatureExtractor,GLPNFeatureExtractor,BeitFeatureExtractor,DeiTFeatureExtractor,DetrFeatureExtractor,YolosFeatureExtractor,DonutFeatureExtractor,NougatImageProcessor,ViTImageProcessor,VitMatteImageProcessor,SamImageProcessor,Swin2SRImageProcessor,Wav2Vec2FeatureExtractor,SeamlessM4TFeatureExtractor,SpeechT5FeatureExtractor,ASTFeatureExtractor,ClapFeatureExtractor};static PROCESSOR_CLASS_MAPPING={WhisperProcessor,Wav2Vec2ProcessorWithLM,SamProcessor,SpeechT5Processor,OwlViTProcessor};static async from_pretrained(e,{progress_callback:o=null,config:i=null,cache_dir:u=null,local_files_only:n=!1,revision:s="main"}={}){let d=i??await getModelJSON(e,"preprocessor_config.json",!0,{progress_callback:o,config:i,cache_dir:u,local_files_only:n,revision:s}),f=d.feature_extractor_type??d.image_processor_type,r=this.FEATURE_EXTRACTOR_CLASS_MAPPING[f];if(!r)if(d.size!==void 0)console.warn(`Feature extractor type "${f}" not found, assuming ImageFeatureExtractor due to size parameter in config.`),r=ImageFeatureExtractor;else throw new Error(`Unknown Feature Extractor type: ${f}`);let a=this.PROCESSOR_CLASS_MAPPING[d.processor_class]??Processor,l=new r(d);return new a(l)}}if(!navigator.gpu){const t="WebGPU is not supported by this browser.";throw alert(t),Error(t)}env.backends.onnx.wasm.proxy=!0;env.backends.onnx.wasm.wasmPaths="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.17.1/dist/";env.backends.onnx.wasm.numThreads=1;env.experimental.useWebGPU=!0;const EXAMPLE_URL="https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024",status=document.getElementById("status"),fileUpload=document.getElementById("upload"),imageContainer=document.getElementById("container"),example=document.getElementById("example");status.textContent="Loading model...";let model;try{model=await AutoModel.from_pretrained("briaai/RMBG-1.4",{config:{model_type:"custom"},quantized:!1})}catch(t){throw status.textContent=t.message,alert(t.message),t}const processor=await AutoProcessor.from_pretrained("briaai/RMBG-1.4",{config:{do_normalize:!0,do_pad:!1,do_rescale:!0,do_resize:!0,image_mean:[.5,.5,.5],feature_extractor_type:"ImageFeatureExtractor",image_std:[1,1,1],resample:2,rescale_factor:.00392156862745098,size:{width:1024,height:1024}}});status.textContent="Warming up...";const[b,c,h,w]=[1,3,1024,1024],dummyData=new Float32Array(b*c*h*w);await model({input:new Tensor("float32",dummyData,[b,c,h,w])});status.textContent="Ready";example.addEventListener("click",t=>{t.preventDefault(),predict(EXAMPLE_URL)});fileUpload.addEventListener("change",function(t){const e=t.target.files[0];if(!e)return;const o=new FileReader;o.onload=i=>predict(i.target.result),o.readAsDataURL(e)});async function predict(t){const e=await RawImage.fromURL(t);imageContainer.innerHTML="",imageContainer.style.backgroundImage=`url(${t})`;const o=e.width/e.height,[i,u]=o>720/480?[720,720/o]:[480*o,480];imageContainer.style.width=`${i}px`,imageContainer.style.height=`${u}px`,status.textContent="Analysing...";const{pixel_values:n}=await processor(e),s=performance.now(),{output:d}=await model({input:n}),f=performance.now(),r=await RawImage.fromTensor(d[0].mul(255).to("uint8")).resize(e.width,e.height),a=document.createElement("canvas");a.width=e.width,a.height=e.height;const l=a.getContext("2d");l.drawImage(e.toCanvas(),0,0);const p=l.getImageData(0,0,e.width,e.height);for(let m=0;m<r.data.length;++m)p.data[4*m+3]=r.data[m];l.putImageData(p,0,0),imageContainer.append(a),imageContainer.style.removeProperty("background-image"),imageContainer.style.background='url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQBAMAAADt3eJSAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAGUExURb+/v////5nD/3QAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAAUSURBVBjTYwABQSCglEENMxgYGAAynwRB8BEAgQAAAABJRU5ErkJggg==")',status.textContent=`Done! (Inference took ${Math.round(f-s)}ms)`}
function __vite__mapDeps(indexes) {
  if (!__vite__mapDeps.viteFileDeps) {
    __vite__mapDeps.viteFileDeps = []
  }
  return indexes.map((i) => __vite__mapDeps.viteFileDeps[i])
}